part b

gistfile1.txt

{
 "metadata": {
  "name": "Ejercicio 2- Parte b "
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from scipy import stats as st\n",
      "from statsmodels.stats.weightstats import CompareMeans, DescrStatsW\n",
      "from statsmodels.iolib import genfromdta\n",
      "import statsmodels.api as sm\n",
      "import matplotlib.cm as cm\n",
      "import matplotlib as mpl\n",
      "import brewer2mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from IPython.display import display, HTML\n",
      "import pscore_match as ps\n",
      "pd.set_option('display.max_columns', 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aguas = pd.read_csv('baseaguas2.csv')\n",
      "bid = pd.read_csv(\"base_bid.csv\")\n",
      "bidaguas = pd.read_csv(\"matching_bidaguas.csv\")\n",
      "bidaguas.sexo = (bidaguas.sexo == 'masculino').astype(int)\n",
      "bidaguas.bedad = bidaguas.bedad.astype(int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aguas['Ypcf'] = aguas.ingresos_hogar/aguas.miembros"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Mean Equality Test"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mean_equality_test():\n",
      "    rows = []\n",
      "    covariates = aguas.loc[aguas.jefe==1,  ['idhogar', 'treatment', 'jefe', 'gender', 'educacion', 'casado', 'extranjero', \\\n",
      "            'empleohh', 'desempleohh', 'bedad', 'electricidad', 'techo_malo', 'ingresos_hogar', 'miembros' ]]\n",
      "    covariates['Ypcf'] = covariates.ingresos_hogar/covariates.miembros.astype(float)\n",
      "    treated, control = covariates.treatment ==1, covariates.treatment==0\n",
      "    for column in ['idhogar', 'gender', 'educacion', 'casado', 'extranjero', \\\n",
      "            'empleohh', 'desempleohh', 'bedad', 'electricidad', 'techo_malo', 'Ypcf' ]:\n",
      "        x = covariates[column]\n",
      "        t, c = x[treated].dropna(), x[control].dropna()\n",
      "        rows.append([column, t.mean(), c.mean(), t.mean() - c.mean(), st.ttest_ind(t, c)[1]])\n",
      "    return pd.DataFrame(rows, columns= ['name', 'treated', 'control', 'diff', 'p-value'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_equality_test()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>name</th>\n",
        "      <th>treated</th>\n",
        "      <th>control</th>\n",
        "      <th>diff</th>\n",
        "      <th>p-value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>      idhogar</td>\n",
        "      <td> 539.164179</td>\n",
        "      <td> 391.500000</td>\n",
        "      <td> 147.664179</td>\n",
        "      <td> 3.058166e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>       gender</td>\n",
        "      <td>   0.335821</td>\n",
        "      <td>   0.230978</td>\n",
        "      <td>   0.104843</td>\n",
        "      <td> 1.765365e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>    educacion</td>\n",
        "      <td>   3.902985</td>\n",
        "      <td>   4.285326</td>\n",
        "      <td>  -0.382341</td>\n",
        "      <td> 1.312714e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>       casado</td>\n",
        "      <td>   0.671642</td>\n",
        "      <td>   0.752717</td>\n",
        "      <td>  -0.081076</td>\n",
        "      <td> 7.018710e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   extranjero</td>\n",
        "      <td>   0.082090</td>\n",
        "      <td>   0.078804</td>\n",
        "      <td>   0.003285</td>\n",
        "      <td> 9.045318e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>     empleohh</td>\n",
        "      <td>   0.514925</td>\n",
        "      <td>   0.519022</td>\n",
        "      <td>  -0.004096</td>\n",
        "      <td> 9.354026e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  desempleohh</td>\n",
        "      <td>   0.179104</td>\n",
        "      <td>   0.211957</td>\n",
        "      <td>  -0.032852</td>\n",
        "      <td> 4.194045e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>        bedad</td>\n",
        "      <td>  43.970149</td>\n",
        "      <td>  44.945652</td>\n",
        "      <td>  -0.975503</td>\n",
        "      <td> 4.789763e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> electricidad</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.983696</td>\n",
        "      <td>   0.016304</td>\n",
        "      <td> 1.375584e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>   techo_malo</td>\n",
        "      <td>   0.940299</td>\n",
        "      <td>   0.687500</td>\n",
        "      <td>   0.252799</td>\n",
        "      <td> 3.360656e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>         Ypcf</td>\n",
        "      <td> 119.908553</td>\n",
        "      <td> 139.961817</td>\n",
        "      <td> -20.053264</td>\n",
        "      <td> 8.704401e-02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "            name     treated     control        diff       p-value\n",
        "0        idhogar  539.164179  391.500000  147.664179  3.058166e-09\n",
        "1         gender    0.335821    0.230978    0.104843  1.765365e-02\n",
        "2      educacion    3.902985    4.285326   -0.382341  1.312714e-02\n",
        "3         casado    0.671642    0.752717   -0.081076  7.018710e-02\n",
        "4     extranjero    0.082090    0.078804    0.003285  9.045318e-01\n",
        "5       empleohh    0.514925    0.519022   -0.004096  9.354026e-01\n",
        "6    desempleohh    0.179104    0.211957   -0.032852  4.194045e-01\n",
        "7          bedad   43.970149   44.945652   -0.975503  4.789763e-01\n",
        "8   electricidad    1.000000    0.983696    0.016304  1.375584e-01\n",
        "9     techo_malo    0.940299    0.687500    0.252799  3.360656e-09\n",
        "10          Ypcf  119.908553  139.961817  -20.053264  8.704401e-02"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Seleccion de ni\u00f1os hasta 6 a\u00f1os"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ninos = aguas[aguas.bedad <= 6]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def extract_result(endog, result, ingreso):\n",
      "    return endog.name, endog.mean(), result.params['treatment'], result.pvalues['treatment'], result.nobs, result.rsquared, ingreso"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dummyfy(dataframe, column):\n",
      "    answer = dataframe.drop(column, axis=1)\n",
      "    for value in dataframe[column].unique()[1:]:\n",
      "        answer[str(value)] = dataframe[column] == value\n",
      "    return answer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def regress(dataset, column, ingreso, dummies):\n",
      "    columns = [column, 'idhogar', 'treatment', 'techo_malo']\n",
      "    if ingreso:\n",
      "        columns.append('Ypcf')\n",
      "    filtered = dataset.loc[:, columns].dropna()\n",
      "    endog = filtered[column]\n",
      "    exog = sm.add_constant(filtered, prepend=True)\n",
      "    if dummies:\n",
      "        exog = dummyfy(exog, 'idhogar').drop(column, axis=1)\n",
      "    else:\n",
      "        exog = exog.drop(column, axis=1)\n",
      "    ols = sm.OLS(endog, exog)\n",
      "    return extract_result(endog, ols.fit(), ingreso)\n",
      "\n",
      "def regress_multiple_columns(dataset, columns, dummies):\n",
      "    results = []\n",
      "    for column in columns:\n",
      "        results.extend([regress(dataset, column, ingreso, dummies) for ingreso in [True, False]])\n",
      "    return results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def replicate(dataset=aguas, dummies = False):\n",
      "    ninos = dataset[dataset.bedad <= 6]\n",
      "    hogares = dataset[dataset.jefe==1]\n",
      "    ninos_columns = ['sangre', 'hijodi', 'durdiarrea1']\n",
      "    hogares_columns = ['gastobidon1',]\n",
      "    results = regress_multiple_columns(ninos, ninos_columns, dummies)\n",
      "    #results += regress_multiple_columns(hogares, hogares_columns)\n",
      "    columns = ['Dependent variable', 'Mean dependent variable', '$I_{it}$', 'p-value', 'Observations', 'R-squared', 'Per capita income']\n",
      "    return pd.DataFrame(results, columns = columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Replicaci\u00f3n sin variables dummies"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "replications = replicate()\n",
      "HTML(replications.to_html())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dependent variable</th>\n",
        "      <th>Mean dependent variable</th>\n",
        "      <th>$I_{it}$</th>\n",
        "      <th>p-value</th>\n",
        "      <th>Observations</th>\n",
        "      <th>R-squared</th>\n",
        "      <th>Per capita income</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      sangre</td>\n",
        "      <td> 0.020979</td>\n",
        "      <td>-0.000758</td>\n",
        "      <td> 0.969531</td>\n",
        "      <td> 286</td>\n",
        "      <td> 0.024089</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      sangre</td>\n",
        "      <td> 0.017857</td>\n",
        "      <td> 0.000089</td>\n",
        "      <td> 0.995827</td>\n",
        "      <td> 336</td>\n",
        "      <td> 0.009530</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      hijodi</td>\n",
        "      <td> 0.154982</td>\n",
        "      <td>-0.115460</td>\n",
        "      <td> 0.023855</td>\n",
        "      <td> 271</td>\n",
        "      <td> 0.055357</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      hijodi</td>\n",
        "      <td> 0.154574</td>\n",
        "      <td>-0.097233</td>\n",
        "      <td> 0.043871</td>\n",
        "      <td> 317</td>\n",
        "      <td> 0.033429</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> durdiarrea1</td>\n",
        "      <td> 0.982517</td>\n",
        "      <td>-1.069338</td>\n",
        "      <td> 0.020032</td>\n",
        "      <td> 286</td>\n",
        "      <td> 0.043258</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> durdiarrea1</td>\n",
        "      <td> 0.925595</td>\n",
        "      <td>-1.022513</td>\n",
        "      <td> 0.012402</td>\n",
        "      <td> 336</td>\n",
        "      <td> 0.032901</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<IPython.core.display.HTML at 0x108857950>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Replicaci\u00f3n con variables dummies"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "replications = replicate(dummies=True)\n",
      "HTML(replications.to_html())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dependent variable</th>\n",
        "      <th>Mean dependent variable</th>\n",
        "      <th>$I_{it}$</th>\n",
        "      <th>p-value</th>\n",
        "      <th>Observations</th>\n",
        "      <th>R-squared</th>\n",
        "      <th>Per capita income</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      sangre</td>\n",
        "      <td> 0.020979</td>\n",
        "      <td>-0.008590</td>\n",
        "      <td> 0.896826</td>\n",
        "      <td> 286</td>\n",
        "      <td> 0.801389</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      sangre</td>\n",
        "      <td> 0.017857</td>\n",
        "      <td>-0.011990</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 336</td>\n",
        "      <td> 0.802020</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      hijodi</td>\n",
        "      <td> 0.154982</td>\n",
        "      <td>-0.178769</td>\n",
        "      <td> 0.293805</td>\n",
        "      <td> 271</td>\n",
        "      <td> 0.805114</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      hijodi</td>\n",
        "      <td> 0.154574</td>\n",
        "      <td>-0.144035</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 317</td>\n",
        "      <td> 0.808896</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> durdiarrea1</td>\n",
        "      <td> 0.982517</td>\n",
        "      <td>-1.403370</td>\n",
        "      <td> 0.323741</td>\n",
        "      <td> 286</td>\n",
        "      <td> 0.832126</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> durdiarrea1</td>\n",
        "      <td> 0.925595</td>\n",
        "      <td>-0.960339</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 336</td>\n",
        "      <td> 0.812489</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<IPython.core.display.HTML at 0x1088427d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Propensity Score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def treatment_effect(column, title, idh = True):\n",
      "    interval = np.logspace(-2,-3)\n",
      "    means = []\n",
      "    ci_min,ci_max = [], []\n",
      "\n",
      "    columns = [column,'treatment', 'Ypcf', 'gender', 'bedad']\n",
      "    if idh:\n",
      "        columns.append('idhogar')\n",
      "    ninos = aguas.loc[aguas.bedad <=6, columns].dropna() #, #'sexo', 'bedad']].copy()\n",
      "    for radius in interval:\n",
      "        psmatch = ps.PropensityScoreMatch(ninos.treatment, ninos.drop('treatment', axis=1), ninos[column], algo='radius', radius=radius)\n",
      "        psmatch.fit()\n",
      "        means.append(psmatch.treatment_effect())\n",
      "        ci = psmatch.confidence_interval()\n",
      "        ci_min.append(ci[0])\n",
      "        ci_max.append(ci[1])\n",
      "    plt.plot(interval, means)\n",
      "    plt.ylabel('treatment effect')\n",
      "    plt.xlabel('radius')\n",
      "    plt.title(title + ' (obs: %d)' % ninos[column].count())\n",
      "    fig = plt.fill_between(interval, ci_min,ci_max, alpha=0.2)\n",
      "    #plt.savefig('pscore.png', format='png')\n",
      "    display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('hijodi', 'presencia de diarrea')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x1095f15d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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uvz8X44wm6rMhhJDQRarPps/RaAkJCZg4cSJSU1PDfmBCCCHnj6DDzQbbZ8Yo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\nG0IIIRH3/wOoAKsQ3tsV3AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('sangre', 'severidad del evento')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x109b1d490>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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TfJCm1BOzXM2oUaPw8MMPY+fOnRAEAY899hgGDuzZ1Md9EatRjDoZWmdEQUC2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\nNhoQTH8OvOexGgSPmgA+dUXSZJNEFGV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      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('durdiarrea1', u'duraci\u00f3n del evento')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x1095c3310>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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UmIsyKUOZYi9oUbn99tuRnp6O9PR0fPXVV7jxxhvx85//PBrZCCGExBlF41TU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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "column = 'hijodi'\n",
      "ninos = aguas.loc[aguas.bedad <=6, [column,'treatment', 'idhogar', 'Ypcf', 'gender', 'bedad']].dropna()\n",
      "psmatch = ps.PropensityScoreMatch(ninos.treatment, ninos.drop('treatment', axis=1), ninos[column], algo='radius', radius=0.005)\n",
      "psmatch.fit()\n",
      "hogar = ninos.idhogar[psmatch.matching_algo.matched.keys()]\n",
      "scores = psmatch.scores[psmatch.matching_algo.matched.keys()]\n",
      "plt.scatter(scores, hogar)\n",
      "plt.ylabel('idhogar')\n",
      "plt.xlabel('propensity score')\n",
      "plt.title('Propensity Score vs IdHogar\\n (presencia de diarrea, radius= 0.005)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x10959de10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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iNDQUALB48WL4+flBo9HolPv6+oLjaB4mQggxNrWSUORyOa5evYr09HR4e3tD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\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('hijodi', 'presencia de diarrea', False)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x109b15050>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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KqUw+wXk2RETeU3yeTaucnBy0tLQgNDTUsU6j0SAnJ8fnYYiIKDh5LDbvvvuu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\nIr9jsSEiIr9jsSEiIr9jsSEiIr/7f+Cuepm7mLNSAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('sangre', 'severidad del evento', False)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x109b1a550>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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JGjVmkpOkymb37t3485//DMA3vb9/pBohZGDcg5yAk5BYE7KyKS8vR11dnVgL\ncxyHN998U/Zg8UiNMxlTJukimStS09SocdZgyiSNGjPJKWRlU1dXh+9///swGAxK5CEkIThpJBpJ\nMJKa0bxer/i91WqN+F08E4Ua+yIok3SRyuUVGNwRqmzU2O5PmaRRYyY59TvrMwDcfPPNKCsrw/nz\n51FeXo69e/diyZIlSmQjJC65aSQaSUAhr7MBgNOnT6O2thZarRYTJ04c8L1sampqsG3bNgBASUkJ\nCgsLwy4rdRt0nQ1RK5vDg4YOV7RjENKnqNzPxi8nJwc5OTmD2pEgCKioqMCKFSsAAGvWrMHYsWPB\ncZyksoWFhWFtgxC1clEzNElAkiqbSLBarbBYLNDr9QCArKwscZ2Usg0NDWCMSd6GGvV1D/tOpwft\nTm+QZ8ivpuYgxo+fELJcWpIWRr1GgUR9v09qEKlcLk/kGtHUeB97yiSNGjPJSbHKpqOjAyaTCeXl\n5QAAo9EIm83WZ0URrCwAydsAgEe2fo7MzEwAQHNzMwBEddnW7sJ79v/D9yaa0fSVbzbtwklTYXN5\nxc5C/y+fUssAJO3/k30HMCKZw3XTpgK40FnuP/gmwnJtbW1Etuf2ChH9+Q3m+YmyXHe0TlV5qqur\nUXe0TlV5ei7LQVKfTSTU19dj+/btKC0tBWMMGzduRFFREcxms+SygiBI3sZHH30E19BLlXhpYalt\n7ECjzYXlN/kmN23tcuNsjNzTRMsBl6QakKRT5gwnHgmM4VhTF6ghjahVVPtsIsFsNqOhoUFctlqt\nfVYS/ZUVBEHyNgDgmlFpEUgeWd8YYcSiikNweQTotbE1W5CHAfXtLoxMM8AQY9nVwu1lVNGQhKTY\nEYPnecyfPx9lZWX4xS9+geLiYvGxyspKVFVVhSzb3zZiwZ49e5CerENBZhIO1NuiHQdA+GP9PYyh\n3uaM2NxefYnn62wi/b6p8VoNyiSNGjPJSbEzGwCYMGECJkzo3Rk9bdo0yWWDrY8l145Kx79OtmFq\nrvrOvKRwexkabE5ckmqATkNnOOGgOdFIolKsz0Zpar7Oxmpz4rEdX2Hr9wphc3pips/mYkkaLugI\ntRS9hvp2+tBoc6ItiqMPCQlFVX02TqeT5kobBPMQA4YadfjybCdy0mL3fXR4GRxdnj4fa+3yYKhR\nh/RkLV0H1YMzQhNwEhJrQraBVFRUBCwLgoDnn39etkDxrGeb//S8NPzrRGsU0/jI1W4sADhnd+Pr\ndidcYR6tMrvgAAAgAElEQVRg47XPhjEW8VtBq7HdnzJJo8ZMcgpZ2dTW1gY+gefR1dUlW6BEce2o\ndPzrRBvitBVTZHcLON3iQLuj7zOgROL2MlCXDVETxhi8AoPLI8Du9sLmlO/vNGgz2oEDB3DgwAE0\nNjZi8+bN4kGxra0NTqdTtkDxrOfV5wWZSeA44GSLA0aDouM0AihxBbMXgLXDBbvbi0yjDvoQgwrU\nOHsAMPhckbqHTU9qvAKdMkkTTibGGBweAZ0uL+zu7v9dXnR2f4nr3F54vAwewfflFRi8zP89Atb7\n/+c4QMNz0PIcNByH5cGnrByUoEe5jIwMFBQU4ODBg8jPzxfX6/V6jBs3Tp40CYTjOEwflY7Pz9hw\nw6UZ0Y6jiHanF+1OL3gO0PMctBoOOp6Hhueg4zlwnG/6fYEBAhgY8y8zxWZJ5oIshep28n0W8+UU\nGADmW2IM6F6El+5hIyuBMbR2eXCu04WmTjea7L6BNwYtjyQtD0OPr6SLvtdruIiMrGTdB3a3l8Et\nMLi9AtxeX/Op2yt0r+v9vau7gnB6LlQavsrkwrJew8Oo52HUaWDS+76MOh4mvQYZyVqMTDPAqOOh\n0/BixeGvRLQ8Bw2P7v97ruPAX/TL3XFKnjsxB61s8vLykJeXB4fDgRtvvFGWnSeai+fWmp6Xhhf+\neQq5GUlRy3T06FcYPfrykOWyhuhhjNDoMoH5BhfAy4A+LnFU65xRasylpkwegcHu8qKq9t8Ydenl\nsHd/4u5y+/63dx803V6G5O6D5IWDZvf/el5c1vC9a3inR8D5ThfO29043+n/cuF8pxstXR6kGDQY\nZtRhmEmHoUYdOI6D3S3g6GkrhqRnwukR4PAIcHZ/OXr8zwFiBaThOPEDj8B8lYjQ/cFBYP71vg8X\n4vfdj2t5DjqN72Cu1/DQaXwfpnT+77s/ZHW0tSBr+FBoNTx0PAe9hkN6shaXpBq63xe+x/uigbaP\n9yOWhGy/mTVrlhI5EtKVI0wYmWrA/9SejVqGTrseNSH27+nuaHj8hlF9HgBI7PA3x7Q5PGhzeNDa\n5en1fafLG9aZpMfLfM03AoNRpwEv6JFhPwujznewNHZ/Ak9N0sKcqoeO59Hl9jX/tHR5cKbNKTYH\ndfaooAxaHqbugy3HAU12NxweAUONOgw36TDUpEdWih5js0xi5RLs7KTa/TUmTux/wl7/GYbDI0Bg\nDBwAnvOdcfMc173sa5XgOYBD9//chXIcIHn0ZXX1WUycGHwGlHhD19lEWSzMjcYYwyt7v0Z+RhL+\n4xvDoh2HhMAYQ6vDA6vNBavNiYZ2F851usQKBfDN4p2erPX9n6RFWrIWaUk6pCVpMcSgQTgfKbS8\n73orvYaL2DB3gTE4uvshOl1eeBkw1KhDapKmV7MPiayoXWdz5MgRvP/++7Db7QHrn3jiiYiHIerE\ncRy+NzELa3eexDhLCrLTotfsRy5gjKGlK7BSabA5YbW5oNNwsAwxwDxEj5z0JFw1cohYuSRpedVf\n+8RzvgrMqNdgeLTDkIgIWdm8/PLLuOOOOzB8+IUfudp/UdVKjfdpkdrmn56swx2Fw/HGfisev3GU\nrO3HauqH6CkauQTG0NblwbnuvolznW6c6+6jONfpgoYJyMk0wTJEj1EZSZiamwpLd5t/tKjx50eZ\noi9kZZOVlUUDBAgA4OqcVByo78DfjzThO1dSc1okeAUmNm+1Otxo7fKgpcsjVixNnW4Y9RoMM/n6\nKYaZ9PjmJUMwzKTH8BQdvvp3LSZO/Ea0XwYhIYXss/nwww+RmpqKKVOmKJUpIqjPRh7tDg9+ufME\nHromG6OiOIouFgiMod3hRUuXbxhus93dXal40Nrl+77T5cWQ7n6T9GT//zoMT9FhmFGPYSYd3c6B\nKCpqfTbl5eXweDzQ6XTiOo7jxLtlksSSmqRF0bgReKOqAU/cOCquZ30WGMP5TjcabE4IIa7HdHsF\nNHd50Gy/ULG0dHmQrOORafSNlMpM1mJEih6XDzd2VyzU4U0SR8jK5o033lAiR0KI5T6bniaNHIID\nX9vwt8NNuH1s5Ltvo9GWzRhDc5cHp1ocONXqwMkWB063OZCk5btvpcChtbUN6el93xZCy3PISNYh\nNz0JEy8ZgkyjFpnJOtlvkKfGdn/KJI0aM8kpevOkkJjFcRwWTMzCs/84AZvT0+cn8yuGG3HVyCED\nHkzicAuwdvhGVlnbnbC75ZktmQFo6/LgVKsDPAeMykhCbnoSbhqdgdz0JAzpMZVQdfU5TJw4UpYc\nhMQ7SdfZ7N69G1arFSUlJWCM4ciRI/jGN9TdKUl9NvI70+Y7A7iYwIB/Hm9FepIWJRNGYJhJ3+92\nBMbwZWMnjpyzw9o9dLfD5UVWil4cvptikG901RCDBrnpyUhPps9ehES1z8br9aKurg4lJSXgOA5v\nvvkmysrKwtpRTU0Ntm3bBgAoKSlBYWH/s70FK3/o0CFs2bIFY8aMwT333BNWBhJZ2WlJQa+5uXZU\nGj6qa8a6j0/h5tGZmHlpRq/ZB7rcXuw91YaP/68VRr0G37wkBVcMz4B5iB6ZRh31ZRASR0I2KNfV\n1eH73//+oG6WJggCKioqsHz5cixfvhwVFRX9Tq3fV3k/t9uNefPmDThLNKnxPi1y3VNDw3O45fKh\n+OkNuTh0thPrPj6Jky2+W1NYbU78+WAjVr5/DCdbHLhvsgWP35CLmy8fikJzCs4c/VKVFY0a7z9C\nmaShTNEnqd3A671wG1ur1Qoh1NCci1itVlgsFuj1vuaUrKwscZ3U8g0NDbBYLBg/fjy+/PLLsPZP\nome4SY9Hr83G52fa8du9XyPTqEOz3Y3peel46lv51HRFSIII+Zd+8803o6ysDOfPn0d5eTn27t2L\nJUuWBC1fU1ODHTt2BKwrKiqCyWQSh0sbjUbYbLaglU1HR0dY5YPpOfrLf1YR7eWe2QBg6rRrka3h\nsX//PgDApEmTAUCx5e9cP1VS+Z1796PT5RFHz/g/lUlZ5jgO+qbjuH0EYLzkUlw5woR/19bgxJEz\nfZafOHFiWNtXctlvIM/neQ7jx09Q1euRY1mNPz//OrXkicTvk9zvV6RJGiBw+vRp1NbWQqvVYsKE\nCcjKygprJ/X19di+fTtKS0vBGMPGjRtRVFQEs7nvGU9Dlf/yyy+xf//+fvtsYmWAQKywu7w40043\nzZOKA6DXcEjqce8Ut8DQYHNFOxoh/ZJrgICkiwBycnIwe/Zs3HLLLWFXNABgNpvR0NAgLlut1qAV\njZTysTpRtRr7bKRmMuo1MOmUuYBTbW3ZXPdXbU2N+P3FXxoOMOl4DDVqMXKIHvmZyRiVkYysIQak\nJeuQpNPAIMMFsGp7rwDKJJUaM8kpZDPa7t27sX//frhcgZ/Iwpn1med5zJ8/XxzBVlxcHPB4ZWUl\nDAaDeCbSX/nt27ejuroara2t6OrqwuLFiyXnIIOTadShsy1xzm6StBzSDFqkGLTgOOBcEsOlQ5P7\nLCvlPiY6je/+J0JsflYiZFBCNqMtW7YMCxYsgMlkClg/ZswYWYMNFjWjyaOh3QGbS54LLNWAB5Bi\n0CDVoIVRhpmTT7d2octDtQ1Rr6hdZ1NcXIxjx44hLy9PbL6iWwwkrgyjHjZX7ws5Y51e4z+L0cg6\n35tBy6PL4w1dkJA4E/Kv6o9//CNOnTqF/fv3o6qqClVVVdi/f78S2eJOLPfZ+CVpeaTKeDU/ENiW\nreM5pBk0GGrUyvZlSdFjVHoSMvq5rTAQmZ+fNsIVmRrb/SmTNGrMJKeQZzbTpk3DzJkz++3QJ4kl\nM1mHDqcXcjSmaTjfzNIjTL5O9aQ4m17fIONN5whRs5B9Ng888ADsdnvM3WKA+mzkdbbDiVZH5JqD\njDoeQ426mLhl8WC4PAJOtMZfMySJH1Hrs3n99dcjvlMS+9KTdWh3RObsxqjjYRli6DV3WjzSa3lo\nOMBLYwRIghlQG8XFw6CJNPHQZ+On1/ARmWrG1EdFo8b3CYhcLr0mcpWqGtv9KZM0aswkp5CVTc9J\nMAHfJJm//vWvZQtEYkdGsg6WFH2vL3OKXtIgApOOhyU1Mc5oeqLbPJNEFPK3vra2NvAJPI+uri7Z\nAsUztd2lExhcJg3PYUiSttdXapIW5iEGZKcaYAxyYE3R+yqavmZ3VuP7BEQuVyRHpKnxTo+USRo1\nZpJT0HaQAwcO4MCBA2hsbMTmzZvFa2za2trgdCbOVeRk4Ix6DYx6DWwOD5q63HB1d1Sk6HmYh/Rd\n0SQCGpFGElHQj1gZGRkoKChAUlIS8vPzUVBQgIKCAkydOhUrVqxQMmPcUGNfhBKZhiRpkZuehOEm\nHVINmpAVjRrfJyByuXQRbEZTY7s/ZZJGjZnkFPTMJi8vD3l5eXA4HLjxxhsVjETiEc9xyEjWhS6Y\nAPQaGpFGEo+kWwzEIrrOhqjZmVYH7J74nWOOxK6o3mKAEBJZei3125DEErKyqa+vx6uvvornnnsO\nzz33HNauXYsnn3xSiWxxR419EZRJukjm0vGR+ZynxnZ/yiSNGjPJKeRv/IYNGzBy5EhkZmZi8uTJ\nGDp0KK677jolshEStyJ5YSchsSBkZaPX6zFnzhxcfvnlyMjIwAMPPIB9+/YpkS3uqPH6EcokXSRz\nRWpEmhqv1aBM0qgxk5xCzjeSnOy7M+GoUaPwt7/9DYWFhWhqagp7RzU1Ndi2bRsAoKSkBIWFhQMq\n/9prr6G+vh6CIODhhx8e0G2qCYk2GpFGEk3Ij1czZ86EzWZDXl4eAGDJkiW4+eabw9qJIAioqKjA\n8uXLsXz5clRUVKC/QXD9lX/wwQexcuVKFBcX4+233w4rR7SpsS+CMkkX6VyGCMwkoMZ2f8okjRoz\nyUnS/Wz8Hn744QHtxGq1wmKxQK/XAwCysrLEdQMtn5SUBK128BNBEhItei0HuyfaKQhRRsSP1jU1\nNdixY0fAuqKiIphMJvEeOEajETabLWhl09HREbL8zp07MXv27H6z7NmzR2xn938qjfZyz2xqyKPG\n5RkzZqgqT89lv0hsz83rMfJyX/Ow/1Ouvx0/lpcnTpyoqjx+1dXVqslz8VmNWvLI2Y8k6aLO3bt3\nw2q1oqSkBIwxHDlyBN/4xjck76S+vh7bt29HaWkpGGPYuHEjioqKgt79M1T5ffv2obGxEXPmzAm6\nT7qok6id3eXFmXaaZ5CoS9Qu6iwvL0ddXZ1Y83EchzfffDOsnZjNZjQ0NIjLVqu139tM91f+2LFj\nOHToUL8VjVqpsS+CMkkX6VzaCAx/VmO7P2WSRo2Z5BSyGa2urg5lZWVYtWrVgHfC8zzmz5+PsrIy\nAEBxcXHA45WVlTAYDOKZSH/lX3jhBQwdOhSrVq1Cbm4uFi1aNOBchEQTjUgjiURSn43Xe+Fe81ar\nFYIQ/pxOEyZMwIQJE/p8rOcghFDlX3rppbD3rRZqvH6EMkknRy6Dhh/UHGlqvFaDMkmjxkxyClnZ\n3HzzzSgrK8P58+dRXl6OvXv3YsmSJUpkIyTu0Yg0kihC9tlcf/31eOCBBzB79mxYLBasWrUq4Wrk\nSFFjXwRlkk6OXIOdI02N7f6USRo1ZpKTpGa0nJwc5OTkyJ2FkIRjiOCN1AhRs5BDn8+dO4fhw4cr\nlSdiaOgziQVur4DjLY5oxyBEFLWhz7/61a8ivlNCiI+ue0QaIfFO0qzPJDLU2BdBmaSTK9dg5khT\nY7s/ZZJGjZnkFPK3/Fvf+ha2bNmCjo6OgC9CSGTQXTtJIgjZZ/PII4/0fhLHqf56F+qzIbGitcuN\ns53uaMcgBIB8fTYhR6O9/PLLEd8pIeQCfQRuNUCI2tFvuYLU2BdBmaSTK5duECME1NjuT5mkUWMm\nOQ2osnE6aaZaQiJFp+FB3TYk3oWsbCoqKgKWBUHA888/L1ugeKbGOb8ok3Ry5tIP8OJONc7mQZmk\nUWMmOYX8Da+trQ18As+jq6tLtkCEJCI9XWxD4lzQyubAgQPYtGkTGhsbsXnzZmzatAmbNm3C+vXr\nqRltgNTYF0GZpJMz10AHCaix3Z8ySaPGTHIKOhotIyMDBQUFOHjwIPLz88X1er0e48aNUyQcIYmC\nRqSReBfyOpu///3vmDVrllJ5IoausyGxhOZII2oRtbnRYrGiISTW6DQ8dHRyQ+KYYr/eNTU1eOaZ\nZ/DMM8/giy++GHD5rVu3YtWqVSgrK0NjY6OckSNOjX0RlEk6uXPpBtCUpsZ2f8okjRozyUnS/WwG\nSxAEVFRUYMWKFQCANWvWYOzYseC4vkfg9Fd+wYIFAIDDhw9jx44dWLx4sRIvgRDZGTQ87O6B3yKa\nEDVT5MzGarXCYrFAr9dDr9cjKysLVqt1UOWPHj2KkSNHyh09otR4/Qhlkk7uXAOZSUCN12pQJmnU\nmElOET+zqampwY4dOwLWFRUVwWQyoby8HABgNBphs9lgsVj63EZHR0e/5VeuXIn29nasXr263yx7\n9uwRDxD+JhBapmW1LjONDlmjfSM9/U0s/gMSLdOykstyCDkaLRLq6+uxfft2lJaWgjGGjRs3oqio\nCGazecDl6+rqUFFRgSeffLLPbahxNFrPyk8tKJN0cudyewWcaHEgnD/I6upq1X1CpkzSqDETEMXR\naJFgNpvR0NAgLlut1qAVjdTy6enpEARq3ybxQ6fhMcBZawhRPUXObADg4MGD2LZtGwCguLgY48eP\nFx+rrKyEwWAIOBMJVn79+vWw2WzQarVYtGhR0KY4NZ7ZEBLKmTYHDRIYBA6+vi8dz4l9YAIDGGMQ\nGCAwBsYAb/f3QvfRT5GDYIyQ68xGscpGaVTZkFh0rsOFFocn2jEGxT/MgecADc9BwwE8x3V/z4Hj\nfAd6rwB4GQv4nrHQB34e3RWKhoNew0PL+yoXrYaHTsOBDzLKtS8XV0L+CqixwwW3EJeHxpCidvM0\nEjlq7IugTNIpkSvcEWmDaffnAGh5X4Wg432VgZbnEe6gOI7zVShcd4Wy7/PPMO2aqWEd9P28Auuu\ngABB8FdEvoO+zl+xaLigl00EE+xn58+uQeD2hhp1sHa4ws4fDrX22ciFKhtCVCSSc6T5m5QMGg46\nDQ8Nx0ErVioctGGeBUjFvJ4Bb1fDc70O/NGQmqRFu8MDu4eaNCOFmtEIURGPwHC8uSusPgT/GYpe\nw8Og5bv7K3zfa/joH7hjVZfbi9NtiTfDPTWjEZIAtDwHLQ/0NUbAf6ai13DQ8bzYZ6HX+pqXSGQl\n6zRIT9Kg1eGNdpS4QAMtFaTGOb8ok3RK5UrWamDU8UhP0mCYSYdLhuiRm56ES4cmIy8jGZekJmF4\nih7pyTpUfVapuopGjT+/gWbKSNZBE+EsfjQ3GiEkqsyphmhHIN10Gh6ZJh3OdbqjHSXmUZ8NIYT0\nQ2AMp1sdcHrj8lDZS0zPIEAIIbGK5zgMNeqiHSPmUWWjoHhqy5aTGjMB6sxFmaQZbKYUgxYp+sge\nLhOtz4YqG0IIkWCoUU8HzEGgPhtCCJHofKcLzV2xPZ1QKNRnQwghUZaRrFPdUPNYQZWNguKxLVsO\naswEqDMXZZImUpk0PIehyZG5YoT6bAghhASVlqxDspbObsJFfTaEEBImu8uLM+3xOW8a9dkQQohK\nGPUapBnkmsgmPlFlo6B4bsuOJDVmAtSZizJJI0emTOPg5k1LtD4bxeZGq6mpEW/zXFJSgsLCwgGX\nd7vd+NGPfoTbbrsNs2bNki80IYQEodPwyDTqcM5O86ZJoUhlIwgCKioqsGLFCgDAmjVrMHbs2KB3\n2wtV/oMPPkBBQUHYd+uLNjXefZIySafGXJRJGrkypSVr0e70DGjetES6SyegUDOa1WqFxWKBXq+H\nXq9HVlYWrFbrgMo7nU7U1NRg8uTJiNOxDYSQGMFzHDKTad40KSJ+ZlNTU4MdO3YErCsqKoLJZEJ5\neTkAwGg0wmazwWKx9LmNjo6OoOXfffddzJo1C62trSGz9LzvuL/NNprLtbW1+MEPfqCaPH4zZsxQ\nTZ6eWdSSx79MP7/Y/fm98sorGDdunCzbH5KkxZ7Pq2BzesSzFX9/TH/LdUfrML94vuTySi7LQZGh\nz/X19di+fTtKS0vBGMPGjRtRVFQEs9kcVvnU1FS8+OKL+H//7/9h165dcDgcQfts1Dj0uWflpxaU\nSTo15qJM0sidyeERcLrVEdbtvKurq1XZlBbTt4U2m81oaGgQl61Wa9CKpr/yVVVVcLvd2LBhA86e\nPQuv14vCwkJkZ2fLmj9S1PYHCFCmcKgxF2WSRu5MSVoe6UlatDikz5smR0VzcS82z/m+AA4c53tc\n/B++b3jO/zxfmY6Ip/JRpLLheR7z589HWVkZAKC4uDjg8crKShgMBvFMJFj5q666Siyza9cuOJ3O\nmKloCCHxLdOog83pgScCbUX+SkPLAxqOA89x0PC+6XJ4/zKH7vWcr1LhOWi4npXKwAZQ1Q8+fp9o\nBgEFJWLzwkCoMROgzlyUSRqlMrU7PLA5Pb6zBY6Df85OXqwEuO5l4MCBKky+apK4zHWX0fSoQKIx\n4raqqip2m9EIISQRpCZpkZok7bCqE9wYIrFsPKAzG0IIISK5zmxouhpCCCGyo8pGQYkyZ9RgqTET\noM5clEkayhR9VNkQQgiRHfXZEEIIEVGfDSGEkJhFlY2C1NhGS5mkU2MuyiQNZYo+qmwIIYTIjvps\nCCGEiKjPhhBCSMyiykZBamyjpUzSqTEXZZKGMkUfVTaEEEJkR302hBBCRNRnQwghJGZRZaMgNbbR\nUibp1JiLMklDmaKPKhtCCCGyU6zPpqamBtu2bQMAlJSUoLCwcEDlX375ZdTX10Ov1+OGG27AjTfe\n2Ofzqc+GEELCF9N36hQEARUVFVixYgUAYM2aNRg7dmzQW572Vd5f2XAch6VLl2LYsGFKRCeEEBIB\nijSjWa1WWCwW6PV66PV6ZGVlwWq1hlW+oaFBfDxWB9CpsY2WMkmnxlyUSRrKFH0Rb0arqanBjh07\nAtYVFRXh888/F5cZY7j22mtx+eWX97mNr776CpWVlX2W37x5M44dO4aUlBTcd999MJvNfW7jo48+\nisCrIYSQxBMTzWjjx4/H+PHjA9bV19ejs7MTpaWlYIxh48aNSE1NDbqNlJSUoOUXLVoEADhx4gTe\neOMNPP74431uQ443ixBCyMAo0oxmNpsDmsGsVmvQMxKp5XU6HbRaRbqcCCGEDJJio9EOHjwoji4r\nLi4OOPuprKyEwWAIGD0WrPx//ud/oqWlBcnJyXjggQcwfPhwJeITQggZhLidroYQQoh60EWdhBBC\nZBcTnR7hXBAarGyw9YcOHcKWLVswZswY3HPPParJ9dprr6G+vh6CIODhhx9GVlZW1DNt3boVR44c\nAc/zWLx4sSoyAYDb7caPfvQj3HbbbZg1a1bUM0m98FjJTE1NTXjppZfg9Xpx6aWX4r777otqJrvd\njnXr1onPPXbsGMrLyyVlkjMXAHz88cd47733oNFocOedd4a8AF2JTB988AF27dqFpKQklJaWwmKx\nKJYp2DEy3Av1wVTO6/Wy5cuXM6fTyZxOJ3vmmWeYIAiSy/a3njHGDh48yD799FO2ZcsWVeS6eBu1\ntbXs1VdfVVWmQ4cOsd/97neqyfTOO++wdevWsb///e9RzeT38ssvs3PnzknKolSm9evXs8OHD6si\n08XbOHHiBPvtb38b9Vx+y5YtY16vl3V2drKnnnoq6pkcDoeYo62tjT3//POKZWKs72NkONv2U30z\nWjgXhAa7GLS/i0THjx+PlJQU1eS6eBtJSUmSR90pleno0aMYOXKkKjI5nU7U1NRg8uTJki/2lft3\nCgj/wmM5MwmCgMbGRlxxxRWqyHTxNt59913JZ6Ry5vL//LKzs/Hll1+iqqoq6LWASmZijMHj8cDt\ndsNkMqG1tRUej0eRTEDfx8hwL9QHYqAZraOjAyaTSTzFNhqNsNlsfZ5GBisLQPI21JZr586dmD17\ntmoyrVy5Eu3t7Vi9erUqMvkPVK2trZLyKJEpOTkZL774YsgLj5XKlJycDJfLhXXr1sFut+M//uM/\nMGXKlKi/TwBgs9nQ1NSEUaNGhcyjVK7x48fjnXfegcfjwa233qqKTPPmzcOzzz6L5ORkdHZ2wm63\n93utYqQyBTtGhlseiIEBAv4LPBcuXIgFCxags7Mz6JscrGw421BTrn379uGSSy6RfBahRKZVq1bh\nkUcewUsvvRT1THa7HYcPH8bEiRMlZVHqfVq0aBHKyspw55134o033oh6ppSUFBiNRixbtgxPP/00\n/vKXv8DlckX9fQKADz/8MOwLsOXM1djYiKqqKjzxxBN4+umn8b//+7+qeK+uueYarFy5Ej/72c+g\n1WolHb8ikSkS2/ZT/ZlNOBeEBisrCEK/2wi3yUOJXMeOHcOhQ4fCGrSgxHsFAOnp6RAEIeqZqqqq\n4Ha7sWHDBpw9exZerxeFhYXIzs6OWqaewrnwWO5Mw4YNQ2trKzIzM1WTyev1oqqqCqtWrZKUR4lc\n9fX18Hq9AHzHBSkVjdyZeqqqqkJeXp5imfwuPkaGe6E+ECPX2QS7wDOci0GDrd++fTuqq6vR2tqK\nMWPGYPHixarI9eijj2Lo0KHgeR65ubniND3RzLR+/XrYbDZotVosWrRIcjOknJn8du3aBafTKbnZ\nQ85MA73wWM5M58+fx2uvvQa73Y5p06ZJbpqVM9PevXthtVoxd+5cSVmUyvXWW2/hyJEjEAQB06dP\nlzyaUM5Mr7zyCurr65GUlITHHntMcstMJDIFO0aG+pu8WExUNoQQQmKb6vtsCCGExD6qbAghhMiO\nKhtCCCGyo8qGEEKI7KiyIUQhb7/9NioqKnqtr6ioQH19fRQSEaIc1V9nQ0i84Diuz/XFxcUKJyFE\neVTZECLB2bNn8dxzz2HKlCk4ePAgDAYDVq5cia6uLmzevBnNzc04d+4crrnmGixcuFB83ubNm/Hl\nlyPYc54AAAIaSURBVF8iMzMTaWlpAdfcvPfee/jXv/6FU6dO4ZlnnkFBQYH42M9//nPce++94rp7\n7rlHnI3A5XJh06ZNOH36NARBwPjx4wP2SYgaUWVDiERWqxW5ubm48847xXXJycm49957kZKSApfL\nhcceewyzZs1CRkYG9u7di1OnTuG5554DAPzqV7/CiBEjxOfeeuutuPXWW/u8gv7is6CeywcPHkR7\nezvWrFkT6ZdIiGyoz4YQicxmM6ZNm9ZrPc/z2L9/P/7xj39Ap9OJk4IePnwY119/PXieB8/zGDt2\n7ICmRrrYFVdcAZvNht/85jf45JNP4Ha7B71NQuRGlQ0hg3Dy5EmsXLkSTU1NyMvLQ2pqqlih8Dwf\nULlEarKO1NRUlJWVYd68eTh58iSefvrpiGyXEDlRZUPIINTW1uKqq67CLbfcAqPRiLNnz4qPjR07\nFpWVlWCMweFwoLq6WvJ2TSYT2traAABHjhwJeIwxBsYYsrOzMW/ePLS0tMDhcETmBREiE+qzIUSi\nvkaTTZ8+HevWrcMXX3yBkSNH4sorrxSb0SZNmoTa2lo88cQTSEtLw7Bhw4KOSLvYrFmz8Oabb+LA\ngQOwWCwBz/v666/xyiuvQKPRwO124+6770ZSUlJkXiQhMqGJOAkhhMiOmtEIIYTIjiobQgghsqPK\nhhBCiOyosiGEECI7qmwIIYTIjiobQgghsqPKhhBCiOz+P+RNrxbtF4K0AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "treatment_effect('durdiarrea1', u'duraci\u00f3n del evento', False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "text": [
        "<matplotlib.collections.PolyCollection at 0x109b00810>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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nQgghvgv6eSrR0dHIyMjADz/8oJoCQgghRN28HlL87LPPBiNHt6LGNlTKJJ8a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\np2l9mYeacu3btw8DBw6UvVcQjEyLFy/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      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}