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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Leveraging-Quantile-Regression-For-A/B-Test\" data-toc-modified-id=\"Leveraging-Quantile-Regression-For-A/B-Test-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Leveraging Quantile Regression For A/B Test</a></span></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>"
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   "source": [
    "# code for loading the format for the notebook\n",
    "import os\n",
    "\n",
    "# path : store the current path to convert back to it later\n",
    "path = os.getcwd()\n",
    "os.chdir(os.path.join('..', '..', 'notebook_format'))\n",
    "from formats import load_style\n",
    "load_style(plot_style=False)"
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "text": [
      "Ethen 2019-01-27 09:16:02 \n",
      "\n",
      "CPython 3.6.4\n",
      "IPython 6.4.0\n",
      "\n",
      "numpy 1.14.2\n",
      "scipy 1.2.0\n",
      "pandas 0.23.4\n",
      "sklearn 0.20.2\n",
      "matplotlib 3.0.2\n",
      "seaborn 0.9.0\n"
     ]
    }
   ],
   "source": [
    "os.chdir(path)\n",
    "\n",
    "# 1. magic for inline plot\n",
    "# 2. magic to print version\n",
    "# 3. magic so that the notebook will reload external python modules\n",
    "# 4. magic to enable retina (high resolution) plots\n",
    "# https://gist.github.com/minrk/3301035\n",
    "%matplotlib inline\n",
    "%load_ext watermark\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%watermark -a 'Ethen' -d -t -v -p numpy,scipy,pandas,sklearn,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leveraging Quantile Regression For A/B Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When launching new features to our product, we often times leverage experiments, or so called A/B tests in order to understand and quantify their impact. Popular statistical methods such as t-test often focuses on calculating **average treatment effects**. Not that there's anything wrong with the approach is just that because average reduces an entire distribution into one single number, any heterogeneity in our distribution may potentially get unnoticed. Keep in mind that in real world settings, negative experience often times stick inside people's head longer and stronger than positive ones. The goal of this notebook is to show how to leverage quantile regression to calculate **quantile treatment effect**, which offers a more precise alternative to only estimating average treatment effects.\n",
    "\n",
    "We will use the NYCflights13 data to conduct our experiments, which contains over 300,000 observations of flights departing NYC in 2013. We will focus on a single variable, the delay time of flights arrival in minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constant column names used across the notebook\n",
    "arr_delay = 'arr_delay'\n",
    "airline_name = 'name'\n",
    "\n",
    "\n",
    "def read_data(path='flights.csv'):\n",
    "    \"\"\"Will try and download the data to local [path] if it doesn't exist.\"\"\"\n",
    "    if not os.path.exists(path):\n",
    "        base_url = 'https://media.githubusercontent.com/media/WillKoehrsen/Data-Analysis/master'\n",
    "        url = base_url + '/univariate_dist/data/formatted_flights.csv'\n",
    "        df = pd.read_csv(url, usecols=[arr_delay, airline_name])\n",
    "        df.to_csv(path, index=False)\n",
    "    else:\n",
    "        df = pd.read_csv(path, usecols=[arr_delay, airline_name])\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(317113, 2)\n"
     ]
    },
    {
     "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>arr_delay</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>United Air Lines Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>United Air Lines Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>American Airlines Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-18.0</td>\n",
       "      <td>JetBlue Airways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-25.0</td>\n",
       "      <td>Delta Air Lines Inc.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   arr_delay                    name\n",
       "0       11.0   United Air Lines Inc.\n",
       "1       20.0   United Air Lines Inc.\n",
       "2       33.0  American Airlines Inc.\n",
       "3      -18.0         JetBlue Airways\n",
       "4      -25.0    Delta Air Lines Inc."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_data()\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start with, we'll use [density plot](https://serialmentor.com/dataviz/histograms-density-plots.html) to visualize the distribution of the `arr_delay` field. For those that are not familiar, think of it as a continuous version of histogram (The plot below overlays density plot on top of histogram)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 486,
       "width": 615
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change default style figure and font size\n",
    "plt.rcParams['figure.figsize'] = 10, 8\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "\n",
    "sns.distplot(df[arr_delay], hist=True, kde=True,  \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's say we would like to use this data and compare the arrive time delay of two airlines and once again we'll use our good old density plot to visualize and compare the two airline's arrival time distribution.\n",
    "\n",
    "> Not affiliated with any one of the airline in anyway and neither is this data guaranteed to be up to date with the status quo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 501,
       "width": 631
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "endeavor_airline = 'Endeavor Air Inc.'\n",
    "us_airway_airline = 'US Airways Inc.'\n",
    "\n",
    "for airline in [endeavor_airline, us_airway_airline]:\n",
    "    subset = df[df[airline_name] == airline]\n",
    "    \n",
    "    sns.distplot(subset[arr_delay], hist=False, kde=True,\n",
    "                 kde_kws={'shade': False, 'linewidth': 3}, label=airline)\n",
    "    \n",
    "plt.legend(prop={'size': 12}, title='Airline')\n",
    "plt.title('Density Plot of Arrival Delays')\n",
    "plt.xlabel('Delay (min)')\n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After visualizing the arrival time on the two airlines, we can see that although both distribution seems to be centered around the same area, the tail-end of both side tells a different story, where one of the airline shows that it has a larger tendency of resulting in a delay.\n",
    "\n",
    "If we were to leverage the two sample t-test to compare the means of two separate samples. We would see that the statistical test would tell us we can't reject the null hypothesis and there is no statistically significant difference between the arrival delay time of the two airlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.7092441508007634, pvalue=0.4781775570341349)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline1_delay = df.loc[df[airline_name] == endeavor_airline, arr_delay]\n",
    "airline2_delay = df.loc[df[airline_name] == us_airway_airline, arr_delay]\n",
    "\n",
    "result = stats.ttest_ind(airline1_delay, airline2_delay, equal_var=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also leverage a single binary variable linear regression to arrive at the same conclusion as the two sample t-test above. The step to do this is do convert our airline variable into a dummy variable and fit a linear regression using the dummy variable as the input feature and the arrival delay time as the response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>US Airways Inc.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   US Airways Inc.\n",
       "0                1\n",
       "1                1\n",
       "2                1\n",
       "3                1\n",
       "4                1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df[airline_name].isin([endeavor_airline, us_airway_airline])\n",
    "df_airline_delay = df[mask].reset_index(drop=True)\n",
    "\n",
    "y = df_airline_delay[arr_delay]\n",
    "X = pd.get_dummies(df_airline_delay[airline_name], drop_first=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using `statsmodel` to build the linear regression as it gives R-like statistical output. For people coming from `scikit-learn`, y variable comes first in statsmodel and by default, it doesn't automatically fit a constant/intercept, so we'll need to add it ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>arr_delay</td>    <th>  R-squared:         </th>  <td>   0.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>  -0.000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>  0.5030</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 27 Jan 2019</td> <th>  Prob (F-statistic):</th>   <td> 0.478</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:16:06</td>     <th>  Log-Likelihood:    </th> <td>-1.7154e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 36125</td>      <th>  AIC:               </th>  <td>3.431e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 36123</td>      <th>  BIC:               </th>  <td>3.431e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>           <td>   -0.2619</td> <td>    0.217</td> <td>   -1.207</td> <td> 0.227</td> <td>   -0.687</td> <td>    0.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>US Airways Inc.</th> <td>   -0.2092</td> <td>    0.295</td> <td>   -0.709</td> <td> 0.478</td> <td>   -0.787</td> <td>    0.369</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10940.592</td> <th>  Durbin-Watson:     </th> <td>   1.381</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>31155.807</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.609</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.215</td>   <th>  Cond. No.          </th> <td>    2.73</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              arr_delay   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                 -0.000\n",
       "Method:                 Least Squares   F-statistic:                    0.5030\n",
       "Date:                Sun, 27 Jan 2019   Prob (F-statistic):              0.478\n",
       "Time:                        09:16:06   Log-Likelihood:            -1.7154e+05\n",
       "No. Observations:               36125   AIC:                         3.431e+05\n",
       "Df Residuals:                   36123   BIC:                         3.431e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "const              -0.2619      0.217     -1.207      0.227      -0.687       0.163\n",
       "US Airways Inc.    -0.2092      0.295     -0.709      0.478      -0.787       0.369\n",
       "==============================================================================\n",
       "Omnibus:                    10940.592   Durbin-Watson:                   1.381\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            31155.807\n",
       "Skew:                           1.609   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.215   Cond. No.                         2.73\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordinary least square\n",
    "model = sm.OLS(y, sm.add_constant(X))\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the numbers for the t-statistic and p-value matches the two-sample t-test result above. The benefit of using a linear regression is that, we can include many other features to see if they are the reasons behind the arrival delay.\n",
    "\n",
    "By looking at average treatment effect, we can see that we would be drawing the conclusion that there is no statistical difference in the two airlines' arrival time, however, based on looking that the distribution of two airline's arrival delay, our hunch tells us that we're probably missing something. This is where estimating quantile treatment effect really provide additional insights not found by simply looking at the average treatment effects. To do so, we change our model to a quantile regression and specify the quantile we are interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>QuantReg Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>arr_delay</td>    <th>  Pseudo R-squared:  </th> <td> 0.01632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>             <td>QuantReg</td>     <th>  Bandwidth:         </th> <td>   2.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>          <td>Least Squares</td>  <th>  Sparsity:          </th> <td>   297.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 27 Jan 2019</td> <th>  No. Observations:  </th>  <td> 36125</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>09:16:06</td>     <th>  Df Residuals:      </th>  <td> 36123</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  Df Model:          </th>  <td>     1</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>           <td>   44.0000</td> <td>    0.692</td> <td>   63.603</td> <td> 0.000</td> <td>   42.644</td> <td>   45.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>US Airways Inc.</th> <td>  -16.0000</td> <td>    0.944</td> <td>  -16.954</td> <td> 0.000</td> <td>  -17.850</td> <td>  -14.150</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                         QuantReg Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:              arr_delay   Pseudo R-squared:              0.01632\n",
       "Model:                       QuantReg   Bandwidth:                       2.677\n",
       "Method:                 Least Squares   Sparsity:                        297.1\n",
       "Date:                Sun, 27 Jan 2019   No. Observations:                36125\n",
       "Time:                        09:16:06   Df Residuals:                    36123\n",
       "                                        Df Model:                            1\n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "const              44.0000      0.692     63.603      0.000      42.644      45.356\n",
       "US Airways Inc.   -16.0000      0.944    -16.954      0.000     -17.850     -14.150\n",
       "===================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.QuantReg(y, sm.add_constant(X))\n",
    "result = model.fit(q=0.9, kernel='gau')\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At 0.9 quantile, we were able to detect a statistically significant effect!\n",
    "\n",
    "We can of course, also compute this across multiples quantiles and plot the quantile treatment effect in a single figure to get a much more nuanced insights into the treatment effect of our experiment that different quantiles. This allows us the detect for extreme scenarios where a single two-sample t-tests would fail raise any concerns towards the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>quantile</th>\n",
       "      <th>US Airways Inc.</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>8.000005</td>\n",
       "      <td>1.040500e-226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6.999999</td>\n",
       "      <td>1.185948e-246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>5.999997</td>\n",
       "      <td>5.233626e-179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.048937e-72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.999995</td>\n",
       "      <td>1.846561e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.999995</td>\n",
       "      <td>6.457823e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7</td>\n",
       "      <td>-1.000001</td>\n",
       "      <td>4.946187e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "      <td>-6.000002</td>\n",
       "      <td>2.991759e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.9</td>\n",
       "      <td>-16.000001</td>\n",
       "      <td>3.194679e-64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantile  US Airways Inc.         pvalue\n",
       "0       0.1         8.000005  1.040500e-226\n",
       "1       0.2         6.999999  1.185948e-246\n",
       "2       0.3         5.999997  5.233626e-179\n",
       "3       0.4         4.000000   4.048937e-72\n",
       "4       0.5         2.999995   1.846561e-34\n",
       "5       0.6         1.999995   6.457823e-12\n",
       "6       0.7        -1.000001   4.946187e-03\n",
       "7       0.8        -6.000002   2.991759e-30\n",
       "8       0.9       -16.000001   3.194679e-64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_quantile_treatment_effect(X, y, quantiles):\n",
    "    coefs = []\n",
    "    pvalues = []\n",
    "    for q in quantiles:\n",
    "        model = sm.QuantReg(y, sm.add_constant(X))\n",
    "        result = model.fit(q=q, kernel='gau')\n",
    "\n",
    "        coef = result.params[1]\n",
    "        coefs.append(coef)\n",
    "\n",
    "        pvalue = result.pvalues[1]\n",
    "        pvalues.append(pvalue)\n",
    "        \n",
    "    coef_name = result.params.index[1]\n",
    "\n",
    "    df_quantile_effect = pd.DataFrame({\n",
    "        'quantile': quantiles,\n",
    "        coef_name: coefs,\n",
    "        'pvalue': pvalues\n",
    "    })\n",
    "    return df_quantile_effect\n",
    "\n",
    "\n",
    "quantiles = np.arange(0.1, 1.0, 0.1)\n",
    "df_quantile_effect = compute_quantile_treatment_effect(X, y, quantiles)\n",
    "df_quantile_effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 431,
       "width": 622
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(df_quantile_effect['quantile'], df_quantile_effect['US Airways Inc.'])\n",
    "ax.set_xlabel('quantiles')\n",
    "ax.set_ylabel('effect size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Blog: Histograms and Density Plots in Python](https://towardsdatascience.com/histograms-and-density-plots-in-python-f6bda88f5ac0)\n",
    "- [Blog: Comparative Statistics in Python using SciPy](http://benalexkeen.com/comparative-statistics-in-python-using-scipy/)\n",
    "- [Blog: Analyzing Experiment Outcomes: Beyond Average Treatment Effects](https://eng.uber.com/analyzing-experiment-outcomes/)\n",
    "- [Online Book: Visualizing distributions: Histograms and density plots](https://serialmentor.com/dataviz/histograms-density-plots.html)"
   ]
  }
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