notebooks/causal_inference_lalonde.ipynb

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        "#@title Copyright 2019 The Empirical Calibration Authors.\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# ============================================================================\n"
      ]
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    {
      "cell_type": "markdown",
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        "id": "at-UGrku-nOR"
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      "source": [
        "# Causal Inference of Lalonde"
      ]
    },
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        "\u003ctable align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/google/empirical_calibration/blob/master/notebooks/causal_inference_lalonde.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/google/empirical_calibration/blob/master/notebooks/causal_inference_lalonde.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
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      "metadata": {
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      "source": [
        "\u003e[Causal Inference of Lalonde](#scrollTo=at-UGrku-nOR)\n",
        "\n",
        "\u003e\u003e[Background](#scrollTo=elFMgW4wr7Dn)\n",
        "\n",
        "\u003e\u003e[Imports](#scrollTo=aUBsEiG8mNd2)\n",
        "\n",
        "\u003e\u003e[Data](#scrollTo=vEpwsBMGYNc7)\n",
        "\n",
        "\u003e\u003e\u003e[Experiment benchmark](#scrollTo=tqfLgPXsmxps)\n",
        "\n",
        "\u003e\u003e\u003e\u003e[Difference Estimator](#scrollTo=lNcbasXRxb2s)\n",
        "\n",
        "\u003e\u003e\u003e\u003e[Regression Estimator](#scrollTo=NwTjJpCawumH)\n",
        "\n",
        "\u003e\u003e\u003e[Observational controls](#scrollTo=C9L8BZJux89B)\n",
        "\n",
        "\u003e\u003e[Empirical Calibration](#scrollTo=3cqeVQRduHSv)\n",
        "\n",
        "\u003e\u003e\u003e[Prepare Balancing Constraints](#scrollTo=l_T8GZvEo3Un)\n",
        "\n",
        "\u003e\u003e\u003e[Entropy Balancing](#scrollTo=XcOC7mKlpKC9)\n",
        "\n",
        "\u003e\u003e\u003e[Quadratic Balancing](#scrollTo=vLijOb5cdpjd)\n",
        "\n",
        "\u003e\u003e\u003e[Imposing an Upper Bound on Weights](#scrollTo=ToPPJFopDrEL)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "grhzvDmPlpgr"
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      "source": [
        "We illustrate the use of empirical calibration on the observational part of the LaLonde data to evaluate the effect of a job training program.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "id": "elFMgW4wr7Dn"
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      "source": [
        "## Background\n",
        "\n",
        "The [LaLonde (1986)](http://people.hbs.edu/nashraf/LaLonde_1986.pdf) data is a\n",
        "canonical benchmark in the causal inference literature. It consists of three\n",
        "groups for evaluating the effect of a large scale job training program — the\n",
        "National Supported Work Demonstration (NSW):\n",
        "\n",
        "*   An experimental treatment group with 185 observations.\n",
        "*   An experimental control group with 260 observations.\n",
        "*   An observational control group drawn from the Current Population Survey\n",
        "    (CPS), with 15,992 observations.\n",
        "\n",
        "The outcome variable is the post-intervention earnings in 1978. The following\n",
        "pre-intervention covariates are available for all three groups.\n",
        "\n",
        "Name         | Description\n",
        "------------ | ---------------------------------------------------\n",
        "age          | age.\n",
        "education    | years of schooling.\n",
        "black        | 1 if black, 0 otherwise.\n",
        "hispanic     | 1 if hispanic, 0 otherwise.\n",
        "married      | 1 if married, 0 otherwise.\n",
        "nodegree     | 1 if not completed high school degree, 0 otherwise.\n",
        "earnings1974 | pre-intervention earnings in 1974.\n",
        "earnings1975 | pre-intervention earnings in 1975.\n"
      ]
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      "source": [
        "## Imports"
      ]
    },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import patsy\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "\n",
        "# install and import ec\n",
        "!pip install -q git+https://github.com/google/empirical_calibration\n",
        "import empirical_calibration as ec\n",
        "\n",
        "sns.set_style('whitegrid')\n",
        "%config InlineBackend.figure_format='retina'\n",
        "%precision 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vEpwsBMGYNc7"
      },
      "source": [
        "## Data\n",
        "\n",
        "The data are downloaded from\n",
        "[Dehejia and Wahba (1999)](http://www.uh.edu/~adkugler/Dehejia\u0026Wahba_JASA.pdf)'s\n",
        "data hosting [website](https://users.nber.org/~rdehejia/data) via functions\n",
        "under `ec.data.lalonde`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tqfLgPXsmxps"
      },
      "source": [
        "### Experiment benchmark\n",
        "\n",
        "The first column is the treatment assignment. The last column is the post-intervention outcome variable. The remaining 8 columns are pre-intervention covariates."
      ]
    },
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              "   treatment   age  education  ...  earnings1974  earnings1975  earnings1978\n",
              "0        1.0  37.0       11.0  ...           0.0           0.0     9930.0460\n",
              "1        1.0  22.0        9.0  ...           0.0           0.0     3595.8940\n",
              "2        1.0  30.0       12.0  ...           0.0           0.0    24909.4500\n",
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              "\n",
              "[5 rows x 10 columns]"
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        "#@title experimentl treatment group\n",
        "treated = ec.data.lalonde.experimental_treated()\n",
        "treated.head()"
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              "      \u003ctd\u003e0.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.0\u003c/td\u003e\n",
              "      \u003ctd\u003e1.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.0\u003c/td\u003e\n",
              "      \u003ctd\u003e0.0\u003c/td\u003e\n",
              "      \u003ctd\u003e11796.47\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "   treatment   age  education  ...  earnings1974  earnings1975  earnings1978\n",
              "0        0.0  23.0       10.0  ...           0.0           0.0          0.00\n",
              "1        0.0  26.0       12.0  ...           0.0           0.0      12383.68\n",
              "2        0.0  22.0        9.0  ...           0.0           0.0          0.00\n",
              "3        0.0  18.0        9.0  ...           0.0           0.0      10740.08\n",
              "4        0.0  45.0       11.0  ...           0.0           0.0      11796.47\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title experimental control group\n",
        "control = ec.data.lalonde.experimental_control()\n",
        "control.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Kee9YuvxwG14"
      },
      "source": [
        "We use two methods to estimate the average treatment effects on the treated.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lNcbasXRxb2s"
      },
      "source": [
        "#### Difference Estimator\n",
        "\n",
        "Using a simple difference in means, we estimate that the program increased the average earnings of the treated by $1794."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 69,
          "status": "ok",
          "timestamp": 1562861398008,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "iz5uTYkXwByW",
        "outputId": "6639e377-812f-4a09-b9b6-5187581ef9a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1794.342"
            ]
          },
          "execution_count": 65,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diff = treated[\"earnings1978\"].mean() - control[\"earnings1978\"].mean()\n",
        "diff"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xubaN2WBwqpr"
      },
      "source": [
        "Using the estimator for the sampling variance (see formula 6.18 of [Imbens and Rubin (2014)](http://www.jakebowers.org/ITVExperiments/imbensrubin6.pdf)), we estimate the 95% $z$-interval of the difference as **`[479, 3109]`**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 96,
          "status": "ok",
          "timestamp": 1562861398226,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "2CwFehoOwsMM",
        "outputId": "9e9a005e-adb0-41b4-df3d-06111a3b7b87"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "670.997"
            ]
          },
          "execution_count": 66,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sd = np.sqrt(\n",
        "    treated[\"earnings1978\"].var(ddof=1) / len(treated[\"earnings1978\"]) +\n",
        "    control[\"earnings1978\"].var(ddof=1) / len(control[\"earnings1978\"]))\n",
        "sd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NwTjJpCawumH"
      },
      "source": [
        "#### Regression Estimator\n",
        "The experimental treated and experimental control group are not perfectly balanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 187
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 91,
          "status": "ok",
          "timestamp": 1562861398399,
          "user": {
            "displayName": "",
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            "userId": ""
          },
          "user_tz": 0
        },
        "id": "r62gh5mHw0Tm",
        "outputId": "33d326ed-f12a-4f8f-aa0b-3fba83523379"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "treatment             0.0          1.0\n",
            "age             25.053846    25.816216\n",
            "education       10.088462    10.345946\n",
            "black            0.826923     0.843243\n",
            "hispanic         0.107692     0.059459\n",
            "married          0.153846     0.189189\n",
            "nodegree         0.834615     0.708108\n",
            "earnings1974  2107.026658  2095.573689\n",
            "earnings1975  1266.909002  1532.055314\n",
            "earnings1978  4554.801126  6349.143530\n"
          ]
        }
      ],
      "source": [
        "df = pd.concat([treated, control])\n",
        "print(df.groupby(\"treatment\").mean().T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ztLs0LMaw2gb"
      },
      "source": [
        "Thus, we can fit a linear outcome regression model to \"clean up\" the imbalance. It yields an estimated average treatment effect on the treated of **`$1698`** with a slightly more efficient 95% confidence interval **`[458, 2938]`**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 595
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 75,
          "status": "ok",
          "timestamp": 1562861398544,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "kcSJPp8yw46N",
        "outputId": "b5248fa8-361a-44e5-bd7c-ae19bf43c883"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:           earnings1978   R-squared:                       0.424\n",
            "Model:                            OLS   Adj. R-squared:                  0.412\n",
            "Method:                 Least Squares   F-statistic:                     35.62\n",
            "Date:                Thu, 11 Jul 2019   Prob (F-statistic):           4.51e-47\n",
            "Time:                        16:09:58   Log-Likelihood:                -4534.2\n",
            "No. Observations:                 445   AIC:                             9086.\n",
            "Df Residuals:                     436   BIC:                             9123.\n",
            "Df Model:                           9                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "================================================================================\n",
            "                   coef    std err          t      P\u003e|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------\n",
            "treatment     1698.3166    630.972      2.692      0.007     458.192    2938.441\n",
            "age             59.7197     41.093      1.453      0.147     -21.046     140.486\n",
            "education      441.4534    114.275      3.863      0.000     216.855     666.052\n",
            "black        -2088.4813   1127.214     -1.853      0.065   -4303.930     126.967\n",
            "hispanic       273.2172   1475.048      0.185      0.853   -2625.871    3172.306\n",
            "married       -162.1316    873.006     -0.186      0.853   -1877.954    1553.691\n",
            "nodegree        89.9847    728.406      0.124      0.902   -1341.639    1521.609\n",
            "earnings1974     0.0840      0.077      1.093      0.275      -0.067       0.235\n",
            "earnings1975     0.0506      0.135      0.375      0.708      -0.215       0.316\n",
            "==============================================================================\n",
            "Omnibus:                      283.309   Durbin-Watson:                   2.060\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3713.374\n",
            "Skew:                           2.531   Prob(JB):                         0.00\n",
            "Kurtosis:                      16.216   Cond. No.                     3.58e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.58e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ],
      "source": [
        "lm = sm.OLS(df[\"earnings1978\"], df.iloc[:, :-1])\n",
        "print(lm.fit().summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "C9L8BZJux89B"
      },
      "source": [
        "### Observational controls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {
          "height": 204
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 5412,
          "status": "ok",
          "timestamp": 1562861404085,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "9oVX4H6v_e9Y",
        "outputId": "455ff4e4-ac16-4d24-984f-38f601e89a74"
      },
      "outputs": [
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              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
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            "text/plain": [
              "   treatment   age  education  ...  earnings1974  earnings1975  earnings1978\n",
              "0        0.0  45.0       11.0  ...     21516.670     25243.550     25564.670\n",
              "1        0.0  21.0       14.0  ...      3175.971      5852.565     13496.080\n",
              "2        0.0  38.0       12.0  ...     23039.020     25130.760     25564.670\n",
              "3        0.0  48.0        6.0  ...     24994.370     25243.550     25564.670\n",
              "4        0.0  18.0        8.0  ...      1669.295     10727.610      9860.869\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "execution_count": 69,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "control = ec.data.lalonde.observational_control()\n",
        "control.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JlYjQl-RTVzW"
      },
      "source": [
        "We create two additional indicators `umemployed1974` and `umemployed1975` based on the earnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "_GniC_zyTmJC"
      },
      "outputs": [],
      "source": [
        "treated[\"unemployed1974\"] = treated[\"earnings1974\"] == 0\n",
        "treated[\"unemployed1975\"] = treated[\"earnings1975\"] == 0\n",
        "control[\"unemployed1974\"] = control[\"earnings1974\"] == 0\n",
        "control[\"unemployed1975\"] = control[\"earnings1975\"] == 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2ywVfDmAuJUj"
      },
      "source": [
        "The observational control group is significantly different from the experimental treatment group."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 221
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1562861404469,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "sdCAzgg2Jgdi",
        "outputId": "56d0f01e-ce13-40a6-c0e3-c95ca40e6886"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "treatment                0.0          1.0\n",
            "age                33.225238    25.816216\n",
            "education          12.027514    10.345946\n",
            "black               0.073537     0.843243\n",
            "hispanic            0.072036     0.059459\n",
            "married             0.711731     0.189189\n",
            "nodegree            0.295835     0.708108\n",
            "earnings1974    14016.800304  2095.573689\n",
            "earnings1975    13650.803376  1532.055314\n",
            "earnings1978    14846.659673  6349.143530\n",
            "unemployed1974      0.119622     0.708108\n",
            "unemployed1975      0.109305     0.600000\n"
          ]
        }
      ],
      "source": [
        "df = pd.concat([treated, control])\n",
        "print(df.groupby(\"treatment\").mean().T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rXu--XGWWb1Y"
      },
      "source": [
        "The experimental treated subjects tend to be younger, with less education, black, single, without high school degree, and have smaller pre-intervention earnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 1000
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 4788,
          "status": "ok",
          "timestamp": 1562861409520,
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            "userId": ""
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        },
        "id": "Bmnt_BR4QgMD",
        "outputId": "e2248589-dd68-4818-8fab-d3d5f7ddee09"
      },
      "outputs": [
        {
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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2b5b7b90\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 177,
              "width": 633
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2bd088d0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 177,
              "width": 633
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
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Ps5AVzcL5YR4TsiJDQAfx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\nYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2ee580d0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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GjQqFQudnUO9o/+8V+8Pw2B/Ghf1hfNgnxmXgwIGqz7qOA1xdXXurOdTO2bNnce/ePbi7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\npv74xz/q9OnTmjVrlpYtW+YYX7N2YH0PpQMAALA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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2e4bb9d0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2a43bbd0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2a687850\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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LS0tLKS0tdfqMEet5J9azSaxCQkIAyMvLqzfuypUrtoOPhw0b5hBXVFRETU1NvTfaWtu0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\nRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2aac3890\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 199,
              "width": 633
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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OHcvFixfZv38/n3/+Ob/61a+YPn26U1x9fT1z585l7969dOvWjfvvvx+r1Up2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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf307d3a90\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 199,
              "width": 633
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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ERERERERERFaCxTwiIiIiIiIiIiIrwWIeERER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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf31b83410\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 199,
              "width": 633
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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bvc3mfQsPD8ff35/z589TVVXl9oTggwcPusRdzcCBAz0eqKamJgD8/PycvgvxDvsfxsDA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\nLDVs2LDYNesDBgxQ1apVlZycrB9++MH2+pkzZ5S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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2fe0c5d0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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S5+uvvwb+b4+/lnHFxcWttnvlyhXHoSMPP/ywS9zJkydpaGho9fQ2e5st+xYWFoavry81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\nuXjxIvfccw/jx49n/vz53HP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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf301e6750\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 181,
              "width": 640
            },
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for name in df.columns[1:]:\n",
        "  axes = df.hist(column=name, by=df[\"treatment\"], figsize=(10, 2))\n",
        "  axes[0].set_xlabel(name)\n",
        "  axes[1].set_xlabel(name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3cqeVQRduHSv"
      },
      "source": [
        "## Empirical Calibration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OlJqDsF7MBBg"
      },
      "source": [
        "Empirical calibration assigns weights to the observational control subjects such that the reweighted control group equates the treated group on moments of covariates. The weights are also sought to be as uniform as possible."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "l_T8GZvEo3Un"
      },
      "source": [
        "### Prepare Balancing Constraints"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qXT_Y7-7Eo70"
      },
      "source": [
        "There are 8 raw pre-intervention covariates. Simply balancing on their first moment might not be sufficient. We follow [Hainmueller (2011)](https://web.stanford.edu/~jhain/Paper/PA2012.pdf)'s analysis and consider a total of 52 balancing constraints, which include single terms, selected one-way interactions, and selected squared terms. Note that balancing on all 52 transformed covariates might be excessive, we choose them simply to reproduce Hainmueller's results.\n",
        "\n",
        "We use [`patsy`](https://patsy.readthedocs.io/en/v0.1.0/index.html)'s formula API to construct the covariate matrices, where \"`:`\" indicates interaction, and \"`-`\" means removes the term on the right."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "cLmtv3x93LrO"
      },
      "outputs": [],
      "source": [
        "formula = (\n",
        "    \"(age + education + black + hispanic + married + nodegree + earnings1974 +\"\n",
        "    \" earnings1975 + unemployed1974 + unemployed1975)**2 + I(age**2) + \"\n",
        "    \"I(education**2) - black:hispanic - education:nodegree - \"\n",
        "    \"earnings1974:earnings1975 - earnings1974:unemployed1974 - \"\n",
        "    \"earnings1975:unemployed1975\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Yg3Ujcu4aeGb"
      },
      "outputs": [],
      "source": [
        "treated2 = patsy.dmatrix(formula, treated, return_type=\"dataframe\").iloc[:, 1:]\n",
        "control2 = patsy.dmatrix(formula, control, return_type=\"dataframe\").iloc[:, 1:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HtcIXHR6oo13"
      },
      "source": [
        "The full list of the 52 balancing constraints is shown below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 901
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 37,
          "status": "ok",
          "timestamp": 1562861409924,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "o9YvekgpXiDh",
        "outputId": "88a56f54-5759-436a-933f-67e8b35d7824"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0: unemployed1974[T.True]\n",
            "1: unemployed1975[T.True]\n",
            "2: unemployed1974[T.True]:unemployed1975[T.True]\n",
            "3: age\n",
            "4: age:unemployed1974[T.True]\n",
            "5: age:unemployed1975[T.True]\n",
            "6: education\n",
            "7: education:unemployed1974[T.True]\n",
            "8: education:unemployed1975[T.True]\n",
            "9: black\n",
            "10: black:unemployed1974[T.True]\n",
            "11: black:unemployed1975[T.True]\n",
            "12: hispanic\n",
            "13: hispanic:unemployed1974[T.True]\n",
            "14: hispanic:unemployed1975[T.True]\n",
            "15: married\n",
            "16: married:unemployed1974[T.True]\n",
            "17: married:unemployed1975[T.True]\n",
            "18: nodegree\n",
            "19: nodegree:unemployed1974[T.True]\n",
            "20: nodegree:unemployed1975[T.True]\n",
            "21: earnings1974\n",
            "22: earnings1974:unemployed1975[T.True]\n",
            "23: earnings1975\n",
            "24: earnings1975:unemployed1974[T.True]\n",
            "25: age:education\n",
            "26: age:black\n",
            "27: age:hispanic\n",
            "28: age:married\n",
            "29: age:nodegree\n",
            "30: age:earnings1974\n",
            "31: age:earnings1975\n",
            "32: education:black\n",
            "33: education:hispanic\n",
            "34: education:married\n",
            "35: education:earnings1974\n",
            "36: education:earnings1975\n",
            "37: black:married\n",
            "38: black:nodegree\n",
            "39: black:earnings1974\n",
            "40: black:earnings1975\n",
            "41: hispanic:married\n",
            "42: hispanic:nodegree\n",
            "43: hispanic:earnings1974\n",
            "44: hispanic:earnings1975\n",
            "45: married:nodegree\n",
            "46: married:earnings1974\n",
            "47: married:earnings1975\n",
            "48: nodegree:earnings1974\n",
            "49: nodegree:earnings1975\n",
            "50: I(age ** 2)\n",
            "51: I(education ** 2)\n"
          ]
        }
      ],
      "source": [
        "for i, name in enumerate(control2.columns):\n",
        "  print(\"%s: %s\" % (i, name))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XcOC7mKlpKC9"
      },
      "source": [
        "### Entropy Balancing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b6J9mTcjh3wM"
      },
      "source": [
        "We first use the default `ENTROPY` objective which mimizes the [Kullback-Leibler](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) divergence between the balancing weights and uniform weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1935,
          "status": "ok",
          "timestamp": 1562861411976,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "S8mlKHD2qano",
        "outputId": "0720e4dc-6dc8-4417-a06e-6ea5a261141c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 76,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights, success = ec.calibrate(covariates=control2.values,\n",
        "                                target_covariates=treated2.values)\n",
        "success"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GNI8gHOoqxbz"
      },
      "source": [
        "It turns out that we need to relax the equality constraint a tiny bit to ensure convergence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1805,
          "status": "ok",
          "timestamp": 1562861413885,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "hM7A-0uNFIGZ",
        "outputId": "637f1e43-1f96-4115-ba8d-49e0fc8debff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 77,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights, success = ec.calibrate(covariates=control2.values,\n",
        "                                target_covariates=treated2.values,\n",
        "                                l2_norm=1e-5)\n",
        "success"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1yP1EVSFrMRn"
      },
      "source": [
        "Top 3 control observations account for 12% of total weight."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 240
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 356,
          "status": "ok",
          "timestamp": 1562861414333,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "e3LeGkydHZau",
        "outputId": "b2d9a63e-69e8-42b7-982f-22b9a23bfc2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x7fbf31da0bd0\u003e"
            ]
          },
          "execution_count": 78,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
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WLLHYlyeffLL+tlNzrewvvviivhQJAKxYsQID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JQ9h+QQp7Cj5hD2mr0sWjifb37jdl5b/cagwFg04FN94HU8PU3IppgB31n1uVg0d0ZY/dpQGZU1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\n/upoNTU1ACLv4DPSxIuVlpYW84cbR4wYEbOv7oprSm3GYK9Pnz5x1+nbt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            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2b5a1cd0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 206,
              "width": 725
            },
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pd.Series(weights).plot(style='o', figsize=(12, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HBNq2zADjb8l"
      },
      "source": [
        "The [effective sample size](https://en.wikipedia.org/wiki/Effective_sample_size) of the control group is merely 103.4, a huge reduction from the original sample size 15992."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 32,
          "status": "ok",
          "timestamp": 1562861414407,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "N-9dtzvffso5",
        "outputId": "6ae45243-4105-47a2-a38f-ab50eaded8b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "103.351"
            ]
          },
          "execution_count": 79,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "effective_control_size = 1.0 / np.square(weights).sum()\n",
        "effective_control_size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_9_aLhbJG6V8"
      },
      "source": [
        "We confirm that the means of the raw covariates are matched after weighting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 421
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 67,
          "status": "ok",
          "timestamp": 1562861414570,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "FZlu3HIOFuyj",
        "outputId": "c6a011dd-99a1-4e22-f748-56a55e421439"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003etreatment\u003c/th\u003e\n",
              "      \u003ctd\u003e1.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eage\u003c/th\u003e\n",
              "      \u003ctd\u003e25.816216\u003c/td\u003e\n",
              "      \u003ctd\u003e25.816215\u003c/td\u003e\n",
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              "    \u003ctr\u003e\n",
              "      \u003cth\u003eeducation\u003c/th\u003e\n",
              "      \u003ctd\u003e10.345946\u003c/td\u003e\n",
              "      \u003ctd\u003e10.345945\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eblack\u003c/th\u003e\n",
              "      \u003ctd\u003e0.843243\u003c/td\u003e\n",
              "      \u003ctd\u003e0.843237\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003ehispanic\u003c/th\u003e\n",
              "      \u003ctd\u003e0.059459\u003c/td\u003e\n",
              "      \u003ctd\u003e0.059458\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003emarried\u003c/th\u003e\n",
              "      \u003ctd\u003e0.189189\u003c/td\u003e\n",
              "      \u003ctd\u003e0.189194\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
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              "      \u003cth\u003enodegree\u003c/th\u003e\n",
              "      \u003ctd\u003e0.708108\u003c/td\u003e\n",
              "      \u003ctd\u003e0.708108\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1974\u003c/th\u003e\n",
              "      \u003ctd\u003e2095.573689\u003c/td\u003e\n",
              "      \u003ctd\u003e2095.573689\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1975\u003c/th\u003e\n",
              "      \u003ctd\u003e1532.055314\u003c/td\u003e\n",
              "      \u003ctd\u003e1532.055314\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1978\u003c/th\u003e\n",
              "      \u003ctd\u003e6349.143530\u003c/td\u003e\n",
              "      \u003ctd\u003e4777.895179\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eunemployed1974\u003c/th\u003e\n",
              "      \u003ctd\u003e0.708108\u003c/td\u003e\n",
              "      \u003ctd\u003e0.708109\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eunemployed1975\u003c/th\u003e\n",
              "      \u003ctd\u003e0.600000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.600000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "                          0            1\n",
              "treatment          1.000000     0.000000\n",
              "age               25.816216    25.816215\n",
              "education         10.345946    10.345945\n",
              "black              0.843243     0.843237\n",
              "hispanic           0.059459     0.059458\n",
              "married            0.189189     0.189194\n",
              "nodegree           0.708108     0.708108\n",
              "earnings1974    2095.573689  2095.573689\n",
              "earnings1975    1532.055314  1532.055314\n",
              "earnings1978    6349.143530  4777.895179\n",
              "unemployed1974     0.708108     0.708109\n",
              "unemployed1975     0.600000     0.600000"
            ]
          },
          "execution_count": 80,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([treated.mean(axis=0),\n",
        "           pd.Series(np.matmul(control.astype('float64').T, weights),\n",
        "                     index=treated.columns)], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_VKV0rs8jgkS"
      },
      "source": [
        "The difference in means between the treated group and the reweighted control group yields an average treatment effect on the treated of **`$1571`**, which reproduces [Hainmueller (2011)](https://web.stanford.edu/~jhain/Paper/PA2012.pdf)'s result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 29,
          "status": "ok",
          "timestamp": 1562861414686,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "Z4msgrKyFgYg",
        "outputId": "86209fab-64b7-4d43-abc7-10f00dc3bb26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1571.248"
            ]
          },
          "execution_count": 81,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diff = treated[\"earnings1978\"].mean() - np.sum(control[\"earnings1978\"] * weights)\n",
        "diff"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EvTXU4nT18VL"
      },
      "source": [
        "Under the homoskedastic assumption (i.e., equal unit-level conditional variance), we estimate the variance using formula prescribed in section 19.7 of [Imbens and Rubin (2015)](https://www.cambridge.org/core/books/causal-inference-for-statistics-social-and-biomedical-sciences/71126BE90C58F1A431FE9B2DD07938AB)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 44,
          "status": "ok",
          "timestamp": 1562861414856,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "cHR8XG_AsJTp",
        "outputId": "3d1565c1-b063-4a75-8869-db0f2756091f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "773.882"
            ]
          },
          "execution_count": 82,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sd = np.sqrt(treated[\"earnings1978\"].var(ddof=1) *\n",
        "             (1 / len(treated[\"earnings1978\"]) + 1 / effective_control_size))\n",
        "sd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "G2y8jDnK3n6g"
      },
      "source": [
        "The 95% confidence interval is **`[55, 3088]`**, wider than the reported **`[97, 3044]`** by [Hainmueller (2011)](https://web.stanford.edu/~jhain/Paper/PA2012.pdf) (it is unclear what method was used there)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 35,
          "status": "ok",
          "timestamp": 1562861414993,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "Rumva9pbZ2MQ",
        "outputId": "1d5fa551-28f4-426d-ddbf-ba5d02693e2c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(54.440, 3088.057)"
            ]
          },
          "execution_count": 83,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(diff - 1.96 * sd, diff + 1.96 * sd)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vLijOb5cdpjd"
      },
      "source": [
        "### Quadratic Balancing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4cZqBZOfxkz0"
      },
      "source": [
        "We then use the `QUADRATIC` objective which minimizes the Euclidean distance between the balancing weights and the uniform weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1490,
          "status": "ok",
          "timestamp": 1562861416584,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "t8_pqfPQdseW",
        "outputId": "a3b0f87c-9c79-47cd-c56a-132442627cd1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 84,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights, success = ec.calibrate(\n",
        "    covariates=control2.values,\n",
        "    target_covariates=treated2.values,\n",
        "    objective=ec.Objective.QUADRATIC,\n",
        "    l2_norm=1e-2)\n",
        "success"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nVof1-lRyEHP"
      },
      "source": [
        "Due to being the explicit optimizing objective, the effective control sample size increases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1562861416769,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "MxtI6b9EdzwK",
        "outputId": "dc17e43b-fc0e-43c0-9cd2-86b406597960"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "150.193"
            ]
          },
          "execution_count": 85,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "effective_control_size = 1.0 / np.square(weights).sum()\n",
        "effective_control_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 240
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 460,
          "status": "ok",
          "timestamp": 1562861417392,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "9Q8x1JbgySTD",
        "outputId": "22bdce3d-04c1-42a3-f074-ddcb850413c7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x7fbf31604a90\u003e"
            ]
          },
          "execution_count": 86,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
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3nII1bdo0xcfHKzc3Vw888IDuuOMOWSwW7d69W1VVVbLb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\nREQkOy5WExEREREREREREZHs/gdqQb9MXlUu0wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf326f5f90\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 206,
              "width": 725
            },
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pd.Series(weights).plot(style='o', figsize=(12, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kGw289mX0Kx1"
      },
      "source": [
        "The point estimate **`$1713`** is getting closer to the experimental benchmark **`$1794`**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 55,
          "status": "ok",
          "timestamp": 1562861417553,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "mYAyLXoGd4Ph",
        "outputId": "a7f808ef-7f41-498b-83b9-90a921131764"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1713.270"
            ]
          },
          "execution_count": 87,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diff = treated[\"earnings1978\"].mean() - np.sum(control[\"earnings1978\"] * weights)\n",
        "diff"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4O9DXNwO1ph0"
      },
      "source": [
        "The 95% confidence interval becomes narrower due to increased effective control sample size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 40,
          "status": "ok",
          "timestamp": 1562861417704,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "u-NuRhRMeBjT",
        "outputId": "a0013255-b6c5-4ec8-b00f-36d9f98147a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "641.958"
            ]
          },
          "execution_count": 88,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sd = np.sqrt(treated[\"earnings1978\"].var(ddof=1) *\n",
        "             (1 / len(treated[\"earnings1978\"]) + 1 / effective_control_size))\n",
        "sd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 69,
          "status": "ok",
          "timestamp": 1562861417870,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "k51Qb5oSeCYN",
        "outputId": "ac5dc1d6-2a97-4142-a0c8-ba7cc0124f17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(455.032, 2971.507)"
            ]
          },
          "execution_count": 89,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(diff - 1.96 * sd, diff + 1.96 * sd)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ToPPJFopDrEL"
      },
      "source": [
        "### Imposing an Upper Bound on Weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PuplyA0QDcmv"
      },
      "source": [
        "For both entropy balancing and quadratic balancing, one can optionally impose an upper bound on the weights to avoid extreme individual weights. For illustration, we impose an upper bound 0.015 on quadratic balancing weights, i.e., no single control individual accounts for 1.5% or more of total weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1198,
          "status": "ok",
          "timestamp": 1562861419151,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "MzynUcRZCfr4",
        "outputId": "2a2ec218-3492-4106-b986-5c8a2c5a3ac1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 90,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights, success = ec.calibrate(\n",
        "    covariates=control2.values,\n",
        "    target_covariates=treated2.values,\n",
        "    objective=ec.Objective.QUADRATIC,\n",
        "    max_weight=0.015,\n",
        "    l2_norm=1e-2)\n",
        "success"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Y_UodakgE49B"
      },
      "source": [
        "We can confirm the resulting weights are indeed capped at 0.015."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 240
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 354,
          "status": "ok",
          "timestamp": 1562861419631,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "WHMFN07YCxpQ",
        "outputId": "d6384b52-d992-489f-ea23-86b3ed974d2c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x7fbf310f4710\u003e"
            ]
          },
          "execution_count": 91,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
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GcrKzdFNclKqKFunbigKdqz0p80KDztWe1LcVBfqqaJFu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\naf1nxlJLaKm+V3vqw9nS3vvN7ZkXGr7nlrnYZGCrf2e4ca1v4lsNF/Ca+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nUKa9aswZIl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\ncjsmq4mIiIiIiIiIiIjI7f4PsTcnKyEAUmUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fbf2fc007d0\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 206,
              "width": 725
            },
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pd.Series(weights).plot(style='o', figsize=(12, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8wDuiHf2E9tq"
      },
      "source": [
        "The cost of this additional constraint is a small loss in effective sample size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 34,
          "status": "ok",
          "timestamp": 1562861419704,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "0OK2v1Y7C_IF",
        "outputId": "fc0a5fcc-064e-49b9-c38a-d6cf2f737a14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "146.060"
            ]
          },
          "execution_count": 92,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "effective_control_size = 1.0 / np.square(weights).sum()\n",
        "effective_control_size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "42-x55nrP8i1"
      },
      "source": [
        "Note that this additional weights constraint is an integral part of the optimization, as opposed to a separate trimming step used with propensity score weighting type method. We don't sacrifice the balancing conditions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 421
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 73,
          "status": "ok",
          "timestamp": 1562861419868,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "eFafRYaSQgqu",
        "outputId": "c2dabeee-2b10-4f85-86bc-e30a71e9277d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003etreatment\u003c/th\u003e\n",
              "      \u003ctd\u003e1.000000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eage\u003c/th\u003e\n",
              "      \u003ctd\u003e25.816216\u003c/td\u003e\n",
              "      \u003ctd\u003e25.814217\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eeducation\u003c/th\u003e\n",
              "      \u003ctd\u003e10.345946\u003c/td\u003e\n",
              "      \u003ctd\u003e10.343364\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eblack\u003c/th\u003e\n",
              "      \u003ctd\u003e0.843243\u003c/td\u003e\n",
              "      \u003ctd\u003e0.843853\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003ehispanic\u003c/th\u003e\n",
              "      \u003ctd\u003e0.059459\u003c/td\u003e\n",
              "      \u003ctd\u003e0.059537\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003emarried\u003c/th\u003e\n",
              "      \u003ctd\u003e0.189189\u003c/td\u003e\n",
              "      \u003ctd\u003e0.191219\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003enodegree\u003c/th\u003e\n",
              "      \u003ctd\u003e0.708108\u003c/td\u003e\n",
              "      \u003ctd\u003e0.713018\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1974\u003c/th\u003e\n",
              "      \u003ctd\u003e2095.573689\u003c/td\u003e\n",
              "      \u003ctd\u003e2095.573688\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1975\u003c/th\u003e\n",
              "      \u003ctd\u003e1532.055314\u003c/td\u003e\n",
              "      \u003ctd\u003e1532.055313\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eearnings1978\u003c/th\u003e\n",
              "      \u003ctd\u003e6349.143530\u003c/td\u003e\n",
              "      \u003ctd\u003e4510.605801\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eunemployed1974\u003c/th\u003e\n",
              "      \u003ctd\u003e0.708108\u003c/td\u003e\n",
              "      \u003ctd\u003e0.710463\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003eunemployed1975\u003c/th\u003e\n",
              "      \u003ctd\u003e0.600000\u003c/td\u003e\n",
              "      \u003ctd\u003e0.601687\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "                          0            1\n",
              "treatment          1.000000     0.000000\n",
              "age               25.816216    25.814217\n",
              "education         10.345946    10.343364\n",
              "black              0.843243     0.843853\n",
              "hispanic           0.059459     0.059537\n",
              "married            0.189189     0.191219\n",
              "nodegree           0.708108     0.713018\n",
              "earnings1974    2095.573689  2095.573688\n",
              "earnings1975    1532.055314  1532.055313\n",
              "earnings1978    6349.143530  4510.605801\n",
              "unemployed1974     0.708108     0.710463\n",
              "unemployed1975     0.600000     0.601687"
            ]
          },
          "execution_count": 93,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([treated.mean(axis=0),\n",
        "           pd.Series(np.matmul(control.astype('float64').T, weights),\n",
        "                     index=treated.columns)], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aJLFL8gTQ6iK"
      },
      "source": [
        "The point estimate is very close to the experimental estimate **`$1794`**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 42,
          "status": "ok",
          "timestamp": 1562861419989,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "Xg8oXd-LQqJQ",
        "outputId": "c2e9f447-5cfd-473a-c487-ae26331a72a0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1838.538"
            ]
          },
          "execution_count": 94,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diff = treated[\"earnings1978\"].mean() - np.sum(control[\"earnings1978\"] * weights)\n",
        "diff"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "",
        "kind": "local"
      },
      "name": "causal_inference_lalonde.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}