Week 1 Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9eb26cb7",
   "metadata": {},
   "source": [
    "# Causal Data Science Week 1 Tutorial\n",
    "\n",
    "This is the first tutorial for the Causal Data Science course. In it, we will give an overview of the python libaries that are used in the course: numpy, pandas, and more. Familiarity with these libraries is necessary to apply the 'DoWhy' package - a library used in implementing causal analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160bdad2",
   "metadata": {},
   "source": [
    "## numpy\n",
    "\n",
    "numpy is a library used for storing data and performing numerical operations on it. The documentation is available at: https://numpy.org/doc/stable/index.html, which is where these examples are drawn from (here we show just some exampes of what we can do with numpy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1da2e1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([6, 7, 8])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we first import numpy under the name 'np'\n",
    "import numpy as np\n",
    "\n",
    "# we convert the python list [6, 7, 8] into a numpy array name 'a'\n",
    "a = np.array([6, 7, 8])\n",
    "\n",
    "# we can view 'a' by calling it\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68d1b933",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "array() takes from 1 to 2 positional arguments but 4 were given",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-3-0af710e0d66a>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;31m# we need to provide a list to create a numpy array\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0ma\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0marray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m3\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m)\u001B[0m    \u001B[1;31m# WRONG, a list, [ ], needs to be provided\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m: array() takes from 1 to 2 positional arguments but 4 were given"
     ]
    }
   ],
   "source": [
    "# we need to provide a list to create a numpy array\n",
    "a = np.array(1, 2, 3, 4)    # WRONG, a list, [ ], needs to be provided"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d235dcc9",
   "metadata": {},
   "source": [
    "numpy arrays can be multidimensional. Here, we used arange(15) to make the list [0, 1, 2, 3, 4, ..., 14], and turn it into a 3 x 5 matrix, and name it 'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a177277b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(15).reshape(3, 5)\n",
    "\n",
    "# we can again view 'b'\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e9648b",
   "metadata": {},
   "source": [
    "We can perform operations on numpy arrays. Some of the key functions include things like addition, subtraction, exponentiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64b2d2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 31, 42, 53])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we create two numpy arrays, 'a' and 'b', and add them together\n",
    "a = np.array([20, 30, 40, 50])\n",
    "b = np.arange(4) # this is the array [0, 1, 2, 3]\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1616264",
   "metadata": {},
   "source": [
    "Arrays can be indexed and sliced the same way as lists in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1372eac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 30])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we view the array 'a' from the first element (labelled 0) to the 2nd element\n",
    "a[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affa0ade",
   "metadata": {},
   "source": [
    "With numpy and the other libraries we are using, you can usually find answers to any questions you have quite easily by googling/StackOverflow e.g. 'how to do integration numpy'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4d5da7",
   "metadata": {},
   "source": [
    "## pandas\n",
    "\n",
    "pandas are used to store and manipulate dataframes, with multiple columns measuring different attributes. They are used as inputs for many machine learning libraries, including the library used in this course: DoWhy. Information and examples are available at https://pandas.pydata.org/pandas-docs/stable/index.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6325dfa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     A          B    C  D      E    F\n0  1.0 2013-01-02  1.0  3   test  foo\n1  1.0 2013-01-02  1.0  3  train  foo\n2  1.0 2013-01-02  1.0  3   test  foo\n3  1.0 2013-01-02  1.0  3  train  foo",
      "text/html": "<div>\n<style scoped>\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</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>A</th>\n      <th>B</th>\n      <th>C</th>\n      <th>D</th>\n      <th>E</th>\n      <th>F</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>2013-01-02</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>test</td>\n      <td>foo</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>2013-01-02</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>train</td>\n      <td>foo</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>2013-01-02</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>test</td>\n      <td>foo</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>2013-01-02</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>train</td>\n      <td>foo</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we first import pandas under the name 'pd'\n",
    "import pandas as pd\n",
    "\n",
    "# we create a dataframe name 'df'. The letters 'A', 'B', 'C', etc. represent the columns and the rest the values for that column\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"A\": 1.0,\n",
    "        \"B\": pd.Timestamp(\"20130102\"),\n",
    "        \"C\": pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n",
    "        \"D\": np.array([3] * 4, dtype=\"int32\"),\n",
    "        \"E\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "        \"F\": \"foo\",\n",
    "    }\n",
    ")\n",
    "\n",
    "# we can view our dataframe by calling it\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b94dcdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can view the first n rows of a dataframe using 'dataframe_name'.head(n)\n",
    "df.head(3) # first three rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "019ce6eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     test\n",
       "1    train\n",
       "2     test\n",
       "3    train\n",
       "Name: E, dtype: category\n",
       "Categories (2, object): ['test', 'train']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can view column(s) from the dataframe by calling the desired column(s)\n",
    "df['E'] # column E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5e52c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can view specific rows using indices\n",
    "df[0:2] # rows 0 to 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af24ddc",
   "metadata": {},
   "source": [
    "Data in a pandas dataframe can be plotted easily using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb484bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we import pyplot with the name 'plt'\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# we create a random pandas series and apply the cumsum function\n",
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range(\"1/1/2000\", periods=1000))\n",
    "\n",
    "ts = ts.cumsum()\n",
    "\n",
    "ts.plot() # this is the command that plots the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2694e289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "2000-01-01    0.383780\n2000-01-02    0.185071\n2000-01-03   -0.260354\n2000-01-04   -0.672512\n2000-01-05   -0.206381\n                ...   \n2002-09-22   -2.670624\n2002-09-23   -5.254740\n2002-09-24   -4.281829\n2002-09-25   -4.059097\n2002-09-26   -7.418167\nFreq: D, Length: 1000, dtype: float64"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts # this is the data plotted above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1fab11e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.675974</td>\n",
       "      <td>-0.112480</td>\n",
       "      <td>-1.576842</td>\n",
       "      <td>-0.680248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>1.431576</td>\n",
       "      <td>1.782294</td>\n",
       "      <td>-1.294200</td>\n",
       "      <td>-0.835886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>3.007125</td>\n",
       "      <td>3.576744</td>\n",
       "      <td>-1.955899</td>\n",
       "      <td>0.693311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>1.342954</td>\n",
       "      <td>3.101564</td>\n",
       "      <td>-0.938249</td>\n",
       "      <td>0.203042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.320267</td>\n",
       "      <td>2.469332</td>\n",
       "      <td>-0.365804</td>\n",
       "      <td>-0.295864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>-38.380571</td>\n",
       "      <td>53.360094</td>\n",
       "      <td>-26.823091</td>\n",
       "      <td>-12.611731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>-40.506372</td>\n",
       "      <td>53.400501</td>\n",
       "      <td>-28.278910</td>\n",
       "      <td>-10.978700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>-38.717493</td>\n",
       "      <td>52.149481</td>\n",
       "      <td>-29.354213</td>\n",
       "      <td>-11.376219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>-39.035855</td>\n",
       "      <td>51.416846</td>\n",
       "      <td>-27.860105</td>\n",
       "      <td>-11.683087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>-39.544411</td>\n",
       "      <td>49.575180</td>\n",
       "      <td>-27.428977</td>\n",
       "      <td>-12.534702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A          B          C          D\n",
       "2000-01-01   0.675974  -0.112480  -1.576842  -0.680248\n",
       "2000-01-02   1.431576   1.782294  -1.294200  -0.835886\n",
       "2000-01-03   3.007125   3.576744  -1.955899   0.693311\n",
       "2000-01-04   1.342954   3.101564  -0.938249   0.203042\n",
       "2000-01-05   0.320267   2.469332  -0.365804  -0.295864\n",
       "...               ...        ...        ...        ...\n",
       "2002-09-22 -38.380571  53.360094 -26.823091 -12.611731\n",
       "2002-09-23 -40.506372  53.400501 -28.278910 -10.978700\n",
       "2002-09-24 -38.717493  52.149481 -29.354213 -11.376219\n",
       "2002-09-25 -39.035855  51.416846 -27.860105 -11.683087\n",
       "2002-09-26 -39.544411  49.575180 -27.428977 -12.534702\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we've created a pandas dataframe again using random data, now with fours different columns 'A', 'B', 'C', and 'D'\n",
    "df = pd.DataFrame(\n",
    "    np.random.randn(1000, 4), index=ts.index, columns=[\"A\", \"B\", \"C\", \"D\"]\n",
    ")\n",
    "\n",
    "# again we apply the cumsum function\n",
    "df = df.cumsum()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "629e2bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29655e91100>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "# we can again plot the data, only now different columns are plotted separately\n",
    "df.plot()\n",
    "\n",
    "plt.legend(loc='best') # each column is plotted as a separate line in the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59211cdd",
   "metadata": {},
   "source": [
    "Data can be read into and written from pandas dataframes using formats like csv and excel files  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d8bf628",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"foo.csv\") # writing to a file called 'foo' with type csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd0783d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>0.675974</td>\n",
       "      <td>-0.112480</td>\n",
       "      <td>-1.576842</td>\n",
       "      <td>-0.680248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>1.431576</td>\n",
       "      <td>1.782294</td>\n",
       "      <td>-1.294200</td>\n",
       "      <td>-0.835886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.007125</td>\n",
       "      <td>3.576744</td>\n",
       "      <td>-1.955899</td>\n",
       "      <td>0.693311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>1.342954</td>\n",
       "      <td>3.101564</td>\n",
       "      <td>-0.938249</td>\n",
       "      <td>0.203042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>0.320267</td>\n",
       "      <td>2.469332</td>\n",
       "      <td>-0.365804</td>\n",
       "      <td>-0.295864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>-38.380571</td>\n",
       "      <td>53.360094</td>\n",
       "      <td>-26.823091</td>\n",
       "      <td>-12.611731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>-40.506372</td>\n",
       "      <td>53.400501</td>\n",
       "      <td>-28.278910</td>\n",
       "      <td>-10.978700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>-38.717493</td>\n",
       "      <td>52.149481</td>\n",
       "      <td>-29.354213</td>\n",
       "      <td>-11.376219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>-39.035855</td>\n",
       "      <td>51.416846</td>\n",
       "      <td>-27.860105</td>\n",
       "      <td>-11.683087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>-39.544411</td>\n",
       "      <td>49.575180</td>\n",
       "      <td>-27.428977</td>\n",
       "      <td>-12.534702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          A          B          C          D\n",
       "0    2000-01-01   0.675974  -0.112480  -1.576842  -0.680248\n",
       "1    2000-01-02   1.431576   1.782294  -1.294200  -0.835886\n",
       "2    2000-01-03   3.007125   3.576744  -1.955899   0.693311\n",
       "3    2000-01-04   1.342954   3.101564  -0.938249   0.203042\n",
       "4    2000-01-05   0.320267   2.469332  -0.365804  -0.295864\n",
       "..          ...        ...        ...        ...        ...\n",
       "995  2002-09-22 -38.380571  53.360094 -26.823091 -12.611731\n",
       "996  2002-09-23 -40.506372  53.400501 -28.278910 -10.978700\n",
       "997  2002-09-24 -38.717493  52.149481 -29.354213 -11.376219\n",
       "998  2002-09-25 -39.035855  51.416846 -27.860105 -11.683087\n",
       "999  2002-09-26 -39.544411  49.575180 -27.428977 -12.534702\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"foo.csv\") # reading from a csv file called 'foo'\n",
    "\n",
    "# we can view the imported dataframe\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c3458c",
   "metadata": {},
   "source": [
    "As with numpy, most of your questions about pandas can be answered with some good googling!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd802e84",
   "metadata": {},
   "source": [
    "## scikit-learn\n",
    "\n",
    "scikit-learn is a library used for tackling machine learning problems. It implements a number of supervised and unsupervised learning algorithms. The documentation is available at https://scikit-learn.org/stable/, and these examples are taken from https://scikit-learn.org/stable/tutorial/basic/tutorial.html.\n",
    "\n",
    "Here, we import some example data from the library and fit a support vector machine (SVM) to classify what digits certain images show."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ca44956",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we import 'datasets' from sklearn and load the 'digits' data\n",
    "from sklearn import datasets\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde8ba53",
   "metadata": {},
   "source": [
    "We have a features data set (digits.data) and a targets data set (digits.target)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a226bd57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b10156cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453e0804",
   "metadata": {},
   "source": [
    "We can learn a model using 'fit', and then estimate targets based on unseen features using 'predict'. In this case, we use all data except the last sample to build the model, and then predict on the last. The model predicts that this last digit is '8'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad804bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100.0, gamma=0.001)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "# we create a svm.SVC object called 'clf'\n",
    "clf = svm.SVC(gamma=0.001, C=100.)\n",
    "\n",
    "# we fit the model using the 'digits' data, leaving out the last data point\n",
    "clf.fit(digits.data[:-1], digits.target[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c7508df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can now use our fitted model to predict the last data point\n",
    "clf.predict(digits.data[-1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67468490",
   "metadata": {},
   "source": [
    "## DoWhy\n",
    "\n",
    "DoWhy (https://microsoft.github.io/dowhy/) is a Python library designed for performing causal inference. This is one of the main libraries we will be using in this course. It allows one quickly to identify and test causal relationships based on data and a causal graph.\n",
    "\n",
    "You will first need to install the library using:\n",
    "pip install dowhy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c80bcb2",
   "metadata": {},
   "source": [
    "We will go through the example at https://microsoft.github.io/dowhy/dowhy_simple_example.html to get a basic overview of the library. It is not expected that you understand all steps of this process now, as this is what you will be learning through the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b87def6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we import the 'dowhy' library, the 'CausalModel' class, and 'dowhy.datasets' (this last for generated simulated data to test)\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets\n",
    "\n",
    "# the code below simple hides some warnings we don't want to see\n",
    "import warnings\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98a7a05",
   "metadata": {},
   "source": [
    "We use a data generator from DoWhy to create a simulated data set where 'beta' is the true causal effect. This data also has a causal graph associated with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aca49afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Z0        Z1        W0        W1        W2        W3        W4    v0  \\\n",
      "0  0.0  0.628159 -1.372488  0.203980  0.363965 -0.199222 -2.416444  True   \n",
      "1  0.0  0.126441 -1.691585  1.368733  0.191013 -1.083003 -0.468292  True   \n",
      "2  0.0  0.397947 -0.881767 -0.420911 -0.346606 -0.395051 -0.303137  True   \n",
      "3  0.0  0.605710 -1.840822  0.332872 -0.802170 -0.205484  0.461057  True   \n",
      "4  1.0  0.252117 -1.461264  0.266171 -1.757877 -0.264414 -0.856998  True   \n",
      "\n",
      "           y  \n",
      "0   8.930818  \n",
      "1  10.675570  \n",
      "2   8.306980  \n",
      "3   8.829256  \n",
      "4   6.687169  \n"
     ]
    }
   ],
   "source": [
    "data = dowhy.datasets.linear_dataset(beta=10,\n",
    "        num_common_causes=5,\n",
    "        num_instruments = 2,\n",
    "        num_samples=10000,\n",
    "        treatment_is_binary=True)\n",
    "\n",
    "# we extract the generated dataframe, and name it 'df'\n",
    "df = data[\"df\"]\n",
    "\n",
    "# we can view the first few entries in the dataframe\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106aa2c8",
   "metadata": {},
   "source": [
    "There is also a causal graph for this data, and we can view it in two different formats: 'dot graph' and 'gml graph'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "791be532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph {v0->y;W0-> v0; W1-> v0; W2-> v0; W3-> v0; W4-> v0;Z0-> v0; Z1-> v0;W0-> y; W1-> y; W2-> y; W3-> y; W4-> y;}\n"
     ]
    }
   ],
   "source": [
    "print(data[\"dot_graph\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fdf2717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph[directed 1node[ id \"y\" label \"y\"]node[ id \"W0\" label \"W0\"] node[ id \"W1\" label \"W1\"] node[ id \"W2\" label \"W2\"] node[ id \"W3\" label \"W3\"] node[ id \"W4\" label \"W4\"]node[ id \"Z0\" label \"Z0\"] node[ id \"Z1\" label \"Z1\"]node[ id \"v0\" label \"v0\"]edge[source \"v0\" target \"y\"]edge[ source \"W0\" target \"v0\"] edge[ source \"W1\" target \"v0\"] edge[ source \"W2\" target \"v0\"] edge[ source \"W3\" target \"v0\"] edge[ source \"W4\" target \"v0\"]edge[ source \"Z0\" target \"v0\"] edge[ source \"Z1\" target \"v0\"]edge[ source \"W0\" target \"y\"] edge[ source \"W1\" target \"y\"] edge[ source \"W2\" target \"y\"] edge[ source \"W3\" target \"y\"] edge[ source \"W4\" target \"y\"]]\n"
     ]
    }
   ],
   "source": [
    "print(data[\"gml_graph\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a179c56f",
   "metadata": {},
   "source": [
    "We create a causal model with the data, the treatment variable, the outcome variable, and the causal graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c8db624",
   "metadata": {},
   "outputs": [],
   "source": [
    "# With graph\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment=data[\"treatment_name\"],\n",
    "        outcome=data[\"outcome_name\"],\n",
    "        graph=data[\"gml_graph\"]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b835489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can visualise the model/graph\n",
    "model.view_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae92e59a",
   "metadata": {},
   "source": [
    "Based on the graph, we can identify computable expressions based on the graph only. We can then evaluate these expressions in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6437e33e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                       \n",
      "─────(E[y|W0,W3,W2,W4,W1])\n",
      "d[v₀]                     \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W3,W2,W4,W1,U) = P(y|v0,W0,W3,W2,W4,W1)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "Estimand expression:\n",
      " ⎡                              -1⎤\n",
      " ⎢    d        ⎛    d          ⎞  ⎥\n",
      "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
      " ⎣d[Z₁  Z₀]    ⎝d[Z₁  Z₀]      ⎠  ⎦\n",
      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z1,Z0})\n",
      "Estimand assumption 2, Exclusion: If we remove {Z1,Z0}→{v0}, then ¬({Z1,Z0}→y)\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect()\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803e4819",
   "metadata": {},
   "source": [
    "We can calculate a causal estimate based on the data and the expressions found in the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "753ef502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                       \n",
      "─────(E[y|W0,W3,W2,W4,W1])\n",
      "d[v₀]                     \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W3,W2,W4,W1,U) = P(y|v0,W0,W3,W2,W4,W1)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W0+W3+W2+W4+W1\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 10.037581755722861\n",
      "\n",
      "Causal Estimate is 10.037581755722861\n"
     ]
    }
   ],
   "source": [
    "causal_estimate = model.estimate_effect(identified_estimand,\n",
    "        method_name=\"backdoor.propensity_score_stratification\")\n",
    "print(causal_estimate)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1fe4b0",
   "metadata": {},
   "source": [
    "Finally, there are a number of techniques we can apply to test whether the estimate is accurate when adding in noise, downsampling, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d4fc297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a random common cause\n",
      "Estimated effect:10.037581755722861\n",
      "New effect:10.047406592881899\n",
      "p value:0.39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model.refute_estimate(identified_estimand, causal_estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fe8f35",
   "metadata": {},
   "source": [
    "Here we've added a random common cause, and it has not significantly affected our estimate. This gives us more confidence in our estimate. There are numerous other refutation techniques available in DoWhy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0797787",
   "metadata": {},
   "source": [
    "The example above uses synthetic data generated according to a causal graph. We can also apply DoWhy to real-world data where we believe there to be an underlying causal graph. In this example, we use DoWhy on the Infant Health and Development Program Dataset (Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240. https://doi.org/10.1198/jcgs.2010.08162). The example is taken from https://microsoft.github.io/dowhy/example_notebooks/dowhy_refutation_testing.html#IHDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8667ce3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   treatment  y_factual  y_cfactual       mu0       mu1        x1        x2  \\\n0       True   5.599916    4.318780  3.268256  6.854457 -0.528603 -0.343455   \n1      False   6.875856    7.856495  6.636059  7.562718 -1.736945 -1.802002   \n2      False   2.996273    6.633952  1.570536  6.121617 -0.807451 -0.202946   \n3      False   1.366206    5.697239  1.244738  5.889125  0.390083  0.596582   \n4      False   1.963538    6.202582  1.685048  6.191994 -1.045229 -0.602710   \n\n         x3        x4        x5  ...  x16  x17  x18  x19  x20  x21  x22  x23  \\\n0  1.128554  0.161703 -0.316603  ...    1    1    1    1    0    0    0    0   \n1  0.383828  2.244320 -0.629189  ...    1    1    1    1    0    0    0    0   \n2 -0.360898 -0.879606  0.808706  ...    1    0    1    1    0    0    0    0   \n3 -1.850350 -0.879606 -0.004017  ...    1    0    1    1    0    0    0    0   \n4  0.011465  0.161703  0.683672  ...    1    1    1    1    0    0    0    0   \n\n   x24  x25  \n0    0    0  \n1    0    0  \n2    0    0  \n3    0    0  \n4    0    0  \n\n[5 rows x 30 columns]",
      "text/html": "<div>\n<style scoped>\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</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>treatment</th>\n      <th>y_factual</th>\n      <th>y_cfactual</th>\n      <th>mu0</th>\n      <th>mu1</th>\n      <th>x1</th>\n      <th>x2</th>\n      <th>x3</th>\n      <th>x4</th>\n      <th>x5</th>\n      <th>...</th>\n      <th>x16</th>\n      <th>x17</th>\n      <th>x18</th>\n      <th>x19</th>\n      <th>x20</th>\n      <th>x21</th>\n      <th>x22</th>\n      <th>x23</th>\n      <th>x24</th>\n      <th>x25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>True</td>\n      <td>5.599916</td>\n      <td>4.318780</td>\n      <td>3.268256</td>\n      <td>6.854457</td>\n      <td>-0.528603</td>\n      <td>-0.343455</td>\n      <td>1.128554</td>\n      <td>0.161703</td>\n      <td>-0.316603</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>6.875856</td>\n      <td>7.856495</td>\n      <td>6.636059</td>\n      <td>7.562718</td>\n      <td>-1.736945</td>\n      <td>-1.802002</td>\n      <td>0.383828</td>\n      <td>2.244320</td>\n      <td>-0.629189</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>2.996273</td>\n      <td>6.633952</td>\n      <td>1.570536</td>\n      <td>6.121617</td>\n      <td>-0.807451</td>\n      <td>-0.202946</td>\n      <td>-0.360898</td>\n      <td>-0.879606</td>\n      <td>0.808706</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>1.366206</td>\n      <td>5.697239</td>\n      <td>1.244738</td>\n      <td>5.889125</td>\n      <td>0.390083</td>\n      <td>0.596582</td>\n      <td>-1.850350</td>\n      <td>-0.879606</td>\n      <td>-0.004017</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>1.963538</td>\n      <td>6.202582</td>\n      <td>1.685048</td>\n      <td>6.191994</td>\n      <td>-1.045229</td>\n      <td>-0.602710</td>\n      <td>0.011465</td>\n      <td>0.161703</td>\n      <td>0.683672</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we first load the data from a URL\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/master/datasets/IHDP/csv/ihdp_npci_1.csv\", header = None)\n",
    "\n",
    "# here we are creating names for the columns\n",
    "col =  [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\" ,]\n",
    "for i in range(1,26):\n",
    "    col.append(\"x\"+str(i))\n",
    "data.columns = col\n",
    "\n",
    "# finally, we change the treatment column to a boolean and view the top of the dataframe\n",
    "data = data.astype({\"treatment\":'bool'}, copy=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90740d2",
   "metadata": {},
   "source": [
    "We can create the DoWhy causal model with the data and the common causes, and visualise the resulting causal graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88ad6e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making all 'x' variables common causes\n",
    "common_causes = []\n",
    "\n",
    "for i in range(1, 26):\n",
    "    common_causes += [\"x\"+str(i)]\n",
    "\n",
    "# creating and viewing the CausalModel\n",
    "ihdp_model = CausalModel(\n",
    "                data=data,\n",
    "                treatment='treatment',\n",
    "                outcome='y_factual',\n",
    "                common_causes=common_causes\n",
    "            )\n",
    "ihdp_model.view_model(layout=\"dot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4686ada",
   "metadata": {},
   "source": [
    "We can identify the causal effect from the causal graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f25a70fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                                                                        \n",
      "────────────(Expectation(y_factual|x8,x11,x3,x18,x12,x17,x23,x10,x19,x15,x6,x7\n",
      "d[treatment]                                                                  \n",
      "\n",
      "                                                 \n",
      ",x14,x4,x16,x13,x21,x20,x1,x2,x5,x22,x9,x24,x25))\n",
      "                                                 \n",
      "Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x8,x11,x3,x18,x12,x17,x23,x10,x19,x15,x6,x7,x14,x4,x16,x13,x21,x20,x1,x2,x5,x22,x9,x24,x25,U) = P(y_factual|treatment,x8,x11,x3,x18,x12,x17,x23,x10,x19,x15,x6,x7,x14,x4,x16,x13,x21,x20,x1,x2,x5,x22,x9,x24,x25)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable(s) found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Identify the causal effect for the ihdp dataset\n",
    "ihdp_identified_estimand = ihdp_model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(ihdp_identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de41f423",
   "metadata": {},
   "source": [
    "We can then calculate the causal estimate using propensity score weighting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "754257f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Causal Estimate is 4.0287482183901755\n"
     ]
    }
   ],
   "source": [
    "ihdp_estimate = ihdp_model.estimate_effect(\n",
    "                    ihdp_identified_estimand,\n",
    "                    method_name=\"backdoor.propensity_score_weighting\"\n",
    "                )\n",
    "\n",
    "print(\"The Causal Estimate is \" + str(ihdp_estimate.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88f8a41",
   "metadata": {},
   "source": [
    "Finally, we test the validity of our estimate by replacing the treatment with a placebo. We see that there is now no effect from the treatment, increasing our confidence in our estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a95b4aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:4.028748218390039\n",
      "New effect:-0.028682062611497406\n",
      "p value:0.42000000000000004\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ihdp_refute_placebo_treatment = ihdp_model.refute_estimate(\n",
    "                                    ihdp_identified_estimand,\n",
    "                                    ihdp_estimate,\n",
    "                                    method_name=\"placebo_treatment_refuter\",\n",
    "                                    placebo_type=\"permute\"\n",
    "                                )\n",
    "\n",
    "print(ihdp_refute_placebo_treatment)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}