examples/minimal_end_to_end_causal_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimal end-to-end causal analysis with ```cause2e```\n",
    "This notebook shows a minimal example of how ```cause2e``` can be used as a standalone package for end-to-end causal analysis. It illustrates how we can proceed in stringing together many causal techniques that have previously required fitting together various algorithms from separate sources with unclear interfaces. Additionally, the numerous techniques have been packed into only two easy-to-use functions for causal discovery and causal estimation. Hopefully, you will find this notebook helpful in guiding you through the process of setting up your own causal analyses for custom problems. The overall structure should always be the same regardless of the application domain. For more advanced features, check out the other notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports\n",
    "By the end of this notebook, you will probably be pleasantly surprised by the fact that we did not have to import lots of different packages to perform a full causal analysis consisting of different subtasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from cause2e import path_mgr, knowledge, discovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up paths to data and output directories\n",
    "This step is conveniently handled by the ```PathManager``` class, which avoids having to wrestle with paths throughout the multistep causal analysis. If we want to perform the analysis in a directory ```'dirname'``` that contains ```'dirname/data'``` and ```'dirname/output'``` as subdirectories, we can also use ```PathManagerQuick``` for an even easier setup. The ```experiment_name``` argument is used for generating output files with meaningful names, in case we want to study multiple scenarios (e.g. with varying model parameters).\n",
    "\n",
    "For this analysis, we use the sprinkler dataset. Unfortunately, there are still some problems to be sorted out with categorical data in the estimation step, but continuous and discrete data work fine. Therefore, we use a version of the dataset where only the seasons ```'Spring'``` and ```'Summer'``` are present, such that we can replace these values by 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cwd = os.getcwd()\n",
    "wd = os.path.dirname(cwd)\n",
    "paths = path_mgr.PathManagerQuick(experiment_name='sprinkler',\n",
    "                                  data_name='sprinkler.csv',\n",
    "                                  directory=wd\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn the causal graph from data and domain knowledge\n",
    "Model-based causal inference leverages qualitative knowledge about pairwise causal connections to obtain unbiased estimates of quantitative causal effects. The qualitative knowledge is encoded in the causal graph, so we must recover this graph before we can start actually estimating the desired effects. For learning the graph from data and domain knowledge, we use the ```StructureLearner``` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = discovery.StructureLearner(paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data\n",
    "The ```StructureLearner``` has reading methods for csv and parquet files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.read_csv(index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in the analysis should be an assessment of which variables we are dealing with. In the sprinkler dataset, each sample tells us \n",
    "- the current season\n",
    "- whether it is raining\n",
    "- whether our lawn sprinkler is activated\n",
    "- whether our lawn is slippery\n",
    "- whether our lawn is wet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Rain', 'Season', 'Slippery', 'Sprinkler', 'Wet'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data\n",
    "As mentioned above, currently there are problems in the estimation step with categorical data, so we use this occasion to showcase ```cause2e```'s built-in preprocessing functionalities. We define a function that replaces instances of ```'Summer'``` by 1, and instances of ```'Spring'``` by 0. Afterwards we apply it to our data and throw out the categorical ```'Season'``` column. For more preprocessing options, check out the pertaining notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_summer(data, col_name):\n",
    "    return (data[col_name] == 'Summer').apply(int)\n",
    "\n",
    "learner.combine_variables(name='Season_binary', func=is_summer, input_cols=['Season'], keep_old=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It necessary to communicate to the ```StructureLearner``` if the variables are discrete, continuous, or both. We check how many unique values each variable takes on in our sample and deduce that all variables are discrete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sprinkler        2\n",
       "Rain             2\n",
       "Wet              2\n",
       "Slippery         2\n",
       "Season_binary    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This information is passed to the ```StructureLearner``` by indicating the exact sets of discrete and continuous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.discrete = learner.variables\n",
    "learner.continuous = set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide domain knowledge\n",
    "Humans can often infer parts of the causal graph from domain knowledge. The nodes are always just the variables in the data, so the problem of finding the right graph comes down to selecting the right edges between them. \n",
    "\n",
    "As a reminder: The correct causal graph has an edge from variable A to variable B if and only if variable A directly influences variable B (changing the value of variable A changes the value of variable B if we keep all other variables fixed).\n",
    "\n",
    "There are three ways of passing domain knowledge for the graph search:\n",
    "- Indicate which edges must be present in the causal graph.\n",
    "- Indicate which edges must not be present in the causal graph.\n",
    "- Indicate a temporal order in which the variables have been created. This is then used to generate forbidden edges, since the future can never influence the past.\n",
    "\n",
    "In this example, we use the ```knowledge.EdgeCreator``` to prescribe that\n",
    "- no variables are direct causes of the season,\n",
    "- the lawn being slippery is not a direct cause of any other variable\n",
    "- turning the sprinkler on or off directly affects the wetness of the lawn,\n",
    "- turning the sprinkler on or off does not directly affect the weather."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_creator = knowledge.EdgeCreator()\n",
    "edge_creator.forbid_edges_from_groups({'Season_binary'}, incoming=learner.variables)\n",
    "edge_creator.forbid_edges_from_groups({'Slippery'}, outgoing=learner.variables)\n",
    "edge_creator.require_edge('Sprinkler', 'Wet')\n",
    "edge_creator.forbid_edge('Sprinkler', 'Rain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a fourth way of passing knowledge which is not used in learning the graph, but in validating the quantitative estimates resulting from our end-to-end causal analysis. We often know beforehand what some of the quantitative effects should look like, e.g.\n",
    "- turning the sprinkler on should have a positive overall effect (-> average treatment effect; read below if you are not familiar with types of causal effects) on the lawn being wet and \n",
    "- making the lawn wet should have a positive overall effect on the lawn being slippery.\n",
    "\n",
    "Instead of checking manually at the end if our expectations have been met, we can automate this validation by using the ```knowledge.ValidationCreator```. For instructiveness, we also add two more validations that should fail: \n",
    "- the sprinkler has a negative natural direct effect on the weather and\n",
    "- the natural indirect effect of the lawn being slippery on the season is between 0.2 and 0.4 (remember to normalize your data before such a validation if they are not measured on the same scale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_creator = knowledge.ValidationCreator()\n",
    "validation_creator.add_expected_effect(('Sprinkler', 'Wet', 'nonparametric-ate'), ('greater', 0))\n",
    "validation_creator.add_expected_effect(('Wet', 'Slippery', 'nonparametric-ate'), ('greater', 0))\n",
    "validation_creator.add_expected_effect(('Sprinkler', 'Rain', 'nonparametric-nde'), ('less', 0))\n",
    "validation_creator.add_expected_effect(('Slippery', 'Season_binary', 'nonparametric-nie'), ('between', 0.2, 0.4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pass the knowledge to the ```StructureLearner``` and check if it has been correctly received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================\n",
      "Showing knowledge for graph search.\n",
      "\n",
      "Causal graph constructed from domain knowledge:\n",
      "(Edges required by domain knowledge in red, remaining allowed edges dotted)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Required edges:\n",
      "('Sprinkler', 'Wet')\n",
      "--------------------\n",
      "Forbidden edges:\n",
      "('Wet', 'Season_binary')\n",
      "('Slippery', 'Season_binary')\n",
      "('Rain', 'Season_binary')\n",
      "('Slippery', 'Slippery')\n",
      "('Slippery', 'Wet')\n",
      "('Slippery', 'Sprinkler')\n",
      "('Sprinkler', 'Season_binary')\n",
      "('Sprinkler', 'Rain')\n",
      "('Slippery', 'Rain')\n",
      "('Season_binary', 'Season_binary')\n",
      "====================\n",
      "Saving knowledge graph to png file.\n"
     ]
    }
   ],
   "source": [
    "learner.set_knowledge(edge_creator=edge_creator, validation_creator=validation_creator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply a structure learning algorithm\n",
    "Now that the ```StructureLearner``` has received the data and the domain knowledge, we can try to recover the original graph using causal discovery methods provided by the internally called ```py-causal``` package. There are many parameters that can be tuned (choice of algorithm, search score, independence test, hyperparameters, ...) and we can get an overview by calling some informative methods of the learner. Reasonable default arguments are provided (FGES with CG-BIC score for possibly mixed datatypes and respecting domain knowledge), so we use these for our minimal example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remember to stop the JVM after you are completely done.\n",
      "Proposed causal graph (edges required by domain knowledge in red, undirected edges in blue):\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\n",
      "The graph is fully directed.\n",
      "\n",
      "--------------------------------\n",
      "The following present edges were forced by domain knowledge:\n",
      "('Sprinkler', 'Wet')\n",
      "--------------------------------\n",
      "The following present edges were not forced by domain knowledge:\n",
      "('Season_binary', 'Sprinkler')\n",
      "('Season_binary', 'Rain')\n",
      "('Rain', 'Wet')\n",
      "('Wet', 'Slippery')\n",
      "--------------------------------\n",
      "The following missing edges were not forbidden by domain knowledge:\n",
      "('Rain', 'Slippery')\n",
      "('Wet', 'Rain')\n",
      "('Season_binary', 'Slippery')\n",
      "('Season_binary', 'Wet')\n",
      "('Sprinkler', 'Slippery')\n",
      "('Rain', 'Sprinkler')\n",
      "('Wet', 'Sprinkler')\n",
      "================================\n",
      "Saving graph to dot file.\n",
      "Knowledge is respected!\n",
      "Saving graph to png file.\n",
      "Knowledge is respected!\n",
      "Saving edge analysis.\n",
      "Saving graph to svg file.\n",
      "Knowledge is respected!\n"
     ]
    }
   ],
   "source": [
    "learner.run_quick_search()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the search is a proposed causal graph. We can ignore the warning about stopping the Java Virtual Machine (needed by ```py-causal``` which is a wrapper around the ```TETRAD``` software that is written in Java) if we do not run into any problems. If the algorithm cannot orient all edges, we need to do this manually. Therefore, the output includes a list of all undirected edges, so we do not miss them in complicated graphs with many variables and edges. In our case, all the edges are already oriented.\n",
    "\n",
    "The result seems reasonable:\n",
    "- The weather depends on the season.\n",
    "- The sprinkler use also depends on the season.\n",
    "- The lawn will be wet if it rains or if the sprinkler is activated.\n",
    "- The lawn will be slippery if it is wet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the graph\n",
    "```Cause2e``` allows us to save the result of our search to different file formats with the ```StructureLearner.save_graphs``` method. The name of the file is determined by the ```experiment_name``` parameter from the ```PathManager```. If the result of the graph search is already a directed acyclic graph that respects our domain knowledge, the graph is automatically saved, as we can see from the above output. Check out the graph postprocessing notebook for information on how to proceed when the result of the search needs further adjustments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate causal effects from the graph and the data\n",
    "After we have successfully recovered the causal graph from data and domain knowledge, we can use it to estimate quantitative causal effects between the variables in the graph. It is pleasant that we can use the same graph and data to estimate multiple causal effects, e.g. the one that the Sprinkler has on the lawn being slippery, as well as the one that the season has on the rain probability, without having to repeat the previous steps. Once we have managed to qualitatively model the data generating process, we are already in a very good position. The remaining challenges can be tackled with the core functionality from the ```DoWhy``` package, which we have wrapped into a single easy-to-use convenience method. Usually, all estimation topics are handled by the ```estimator.Estimator```, but the ```StructureLearner``` has the possibility to run a quick analysis of all causal effects with preset parameters. For more detailed analyses, check out the other notebooks that describe the causal identification and estimation process step by step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing and saving heatmaps of the causal estimates.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\n",
      "Passed validations (2/4): \n",
      "Estimated ate of Wet on Slippery: 1.00 (expected: greater than 0) -> True \n",
      "Estimated ate of Sprinkler on Wet: 0.58 (expected: greater than 0) -> True \n",
      " \n",
      "Failed validations (2/4): \n",
      "Estimated nde of Sprinkler on Rain: 0.00 (expected: less than 0) -> False \n",
      "Estimated nie of Slippery on Season_binary: 0.00 (expected: between 0.2 and 0.4) -> False \n",
      "\n",
      "================================\n",
      "10 Largest Average Treatment Effects\n",
      " (direct + indirect influences):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "42            Wet       Slippery             1.00\n",
      "51           Rain            Wet             0.65\n",
      "57           Rain       Slippery             0.65\n",
      "6       Sprinkler            Wet             0.58\n",
      "12      Sprinkler       Slippery             0.58\n",
      "15  Season_binary      Sprinkler             0.48\n",
      "24  Season_binary           Rain            -0.33\n",
      "21  Season_binary            Wet             0.15\n",
      "27  Season_binary       Slippery             0.15\n",
      "3       Sprinkler  Season_binary             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "10 Largest Natural Direct Effects\n",
      " (direct influences only):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "43            Wet       Slippery             1.00\n",
      "52           Rain            Wet             0.65\n",
      "7       Sprinkler            Wet             0.58\n",
      "16  Season_binary      Sprinkler             0.48\n",
      "25  Season_binary           Rain            -0.33\n",
      "4       Sprinkler  Season_binary             0.00\n",
      "10      Sprinkler           Rain             0.00\n",
      "13      Sprinkler       Slippery             0.00\n",
      "22  Season_binary            Wet             0.00\n",
      "28  Season_binary       Slippery             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "10 Largest Natural Indirect Effects\n",
      " (indirect influences only):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "59           Rain       Slippery             0.66\n",
      "14      Sprinkler       Slippery             0.58\n",
      "23  Season_binary            Wet            -0.23\n",
      "29  Season_binary       Slippery            -0.23\n",
      "5       Sprinkler  Season_binary             0.00\n",
      "8       Sprinkler            Wet             0.00\n",
      "11      Sprinkler           Rain             0.00\n",
      "17  Season_binary      Sprinkler             0.00\n",
      "26  Season_binary           Rain             0.00\n",
      "32            Wet      Sprinkler             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "Only quantitative estimates are shown. For methodic details, use Estimator.show_quick_result_methods.\n",
      "\n",
      "Average treatment effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                            \n",
      "Outcome                   Rain Season_binary  Slippery Sprinkler       Wet\n",
      "Treatment                                                                 \n",
      "Rain                  1.000000           0.0  0.655000  0.000000  0.655000\n",
      "Season_binary        -0.331177           1.0  0.145809  0.475292  0.145809\n",
      "Slippery              0.000000           0.0  1.000000  0.000000  0.000000\n",
      "Sprinkler             0.000000           0.0  0.583000  1.000000  0.583000\n",
      "Wet                   0.000000           0.0  1.000000  0.000000  1.000000\n",
      "\n",
      "================================\n",
      "\n",
      "Natural direct effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                        \n",
      "Outcome                   Rain Season_binary Slippery Sprinkler    Wet\n",
      "Treatment                                                             \n",
      "Rain                  1.000000           0.0      0.0  0.000000  0.655\n",
      "Season_binary        -0.331177           1.0      0.0  0.475292  0.000\n",
      "Slippery              0.000000           0.0      1.0  0.000000  0.000\n",
      "Sprinkler             0.000000           0.0      0.0  1.000000  0.583\n",
      "Wet                   0.000000           0.0      1.0  0.000000  1.000\n",
      "\n",
      "================================\n",
      "\n",
      "Natural indirect effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                            \n",
      "Outcome                   Rain Season_binary  Slippery Sprinkler       Wet\n",
      "Treatment                                                                 \n",
      "Rain                       0.0           0.0  0.655000       0.0  0.000000\n",
      "Season_binary              0.0           0.0 -0.230701       0.0 -0.230701\n",
      "Slippery                   0.0           0.0  0.000000       0.0  0.000000\n",
      "Sprinkler                  0.0           0.0  0.583000       0.0  0.000000\n",
      "Wet                        0.0           0.0  0.000000       0.0  0.000000\n",
      "Saving all causal estimates from quick analyses to csv.\n",
      "\n",
      "Successfully generated report in c:\\Users\\D.Gruenbaum\\Desktop\\cause2e_bugfix\\cause2e\\output\\sprinkler\\sprinkler_report.pdf.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learner.run_all_quick_analyses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output consists of a detailed analysis of the causal effects in our system.\n",
    "### Heatmaps\n",
    "The first three images are heatmaps, where the (i, j)-entry shows the causal effect of variable i on variable j. The three heatmaps differ in the type of causal effect that they are describing:\n",
    "  - **Average Treatment Effect (ATE)**: Shows how the outcome variable varies if we vary the treatment variable. This comprises direct and indirect effects. The sprinkler influences the lawn being slippery, even if this does not happen directly, but via its influence on the lawn being wet.\n",
    "  - **Natural Direct Effect (NDE)**: Shows how the outcome variable varies if we vary the treatment variable and keep all other variables fixed. This comprises only direct effects. The sprinkler does not directly influence the lawn being slippery, as we can read off from the heatmap.\n",
    "  - **Natural Indirect Effect (NIE)**: Shows the difference between ATE and NDE. By definition, this comprises only indirect effects. The sprinkler has a strong indirect influence on the lawn being slippery, as we can read off from the heatmap.\n",
    "\n",
    "In our example, we can easily identify from the graph if an effect is direct or indirect, but in examples where a variable simultaneously has a direct and an indirect influence on another variable, it is very challenging to separate the effects without resorting to the algebraic methods that ```cause2e``` uses internally.\n",
    "\n",
    "### Validations\n",
    "The next output shows if our model has passed each of the validations, based on the expected causal effects that we have communicated before running the causal end-to-end analysis. If we are interested in a specific effect, say, the effect of the sprinkler on the lawn being slippery, the estimation of this effect by our learnt causal model can be trusted more if the estimation for other effects match our expectations. We see that the results of the validations turned out exactly as described above (in practice we would not want validations to fail, this was only for demonstrative purposes).\n",
    "\n",
    "### Numeric tables\n",
    "The three numeric tables show the same information as the three previous heatmaps, only in quantitative instead of visual form.\n",
    "\n",
    "### PDF report\n",
    "```Cause2e``` automatically generates a pdf report that contains\n",
    "- the causal graph indicating all qualitative relationships,\n",
    "- the three heatmaps visualizing all quantitative causal effects,\n",
    "- the results of the validations,\n",
    "- the three numeric tables reporting all quantitative causal effects.\n",
    "\n",
    "This is helpful if we want to communicate our findings to other people, or if we want to modify the analysis at a later time and compare the outcome for both methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion of the results\n",
    "The heatmaps show the effects that we would expect given our causal graph: \n",
    "- There is less rain in summer than in spring.\n",
    "- Sprinklers are more often turned on in summer than in spring.\n",
    "- Rain increases the wetness of the lawn.\n",
    "- Turning the sprinkler on also increases the wetness of the lawn.\n",
    "- Wetting the lawn causes it to be slippery.\n",
    "\n",
    "It is interesting to see that the first two effects roughly cancel each other out, resulting in a small ATE of 0.1 that ```'Season_binary'``` has on ```'Slippery'``` and ```'Wet'```. In general, it is a good strategy to look at the heatmaps for discovering the qualitative nature of the different causal effects and then inspect the numeric tables for the exact numbers if needed.\n",
    "\n",
    "Another noteworthy entry is the overall effect of ```'Sprinkler'``` on ```'Wet'```. The result is 0.638, so turning on the sprinkler makes it more likely for the lawn to be wet, as it should be. However, we might ask ourselves: \"Why is the effect not 1? Whenever we turn on the sprinkler, the lawn will be wet!\" This can be explained by looking at the definition of our chosen effect type, the nonparametric average treatment effect (ATE): The ATE tells us how much (on average) we change the outcome by changing the treatment. In our case, we can distinguish between two possible scenarios:\n",
    "If it is raining, then the lawn is wet anyway, so turning the sprinkler on does not change the outcome at all. Only if it is not raining, the lawn state is changed to wet by turning on the sprinkler.\n",
    "\n",
    "We can convince ourselves that this is the correct explanation by looking at the proportion of samples where it is not raining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/latex": [
       "$\\displaystyle 0.583$"
      ],
      "text/plain": [
       "0.583"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - sum(learner.data['Rain']) / len(learner.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recover the same number of 0.638. Additionally, we can change our data to consist only of the instances where it is not raining. If we now repeat the causal analysis, the effect is indeed 1 (skip after the warnings that are caused by the now degenerate dataset). This procedure can be generalized to analyzing other conditional causal effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1830: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return np.sqrt(eigvals[0]/eigvals[-1])\n",
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\base\\model.py:1362: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return self.params / self.bse\n",
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1685: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return 1 - self.ssr/self.centered_tss\n",
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1774: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return self.mse_model/self.mse_resid\n",
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:889: RuntimeWarning: divide by zero encountered in log\n",
      "  llf = -nobs2*np.log(2*np.pi) - nobs2*np.log(ssr / nobs) - nobs2\n",
      "C:\\Users\\D.Gruenbaum\\Anaconda3\\envs\\test\\lib\\site-packages\\statsmodels\\stats\\stattools.py:46: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dw = np.sum(diff_resids**2, axis=axis) / np.sum(resids**2, axis=axis)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing and saving heatmaps of the causal estimates.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\n",
      "Passed validations (2/4): \n",
      "Estimated ate of Wet on Slippery: 1.00 (expected: greater than 0) -> True \n",
      "Estimated ate of Sprinkler on Wet: 1.00 (expected: greater than 0) -> True \n",
      " \n",
      "Failed validations (2/4): \n",
      "Estimated nde of Sprinkler on Rain: 0.00 (expected: less than 0) -> False \n",
      "Estimated nie of Slippery on Season_binary: 0.00 (expected: between 0.2 and 0.4) -> False \n",
      "\n",
      "================================\n",
      "10 Largest Average Treatment Effects\n",
      " (direct + indirect influences):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "42            Wet       Slippery             1.00\n",
      "6       Sprinkler            Wet             1.00\n",
      "12      Sprinkler       Slippery             1.00\n",
      "15  Season_binary      Sprinkler             0.50\n",
      "21  Season_binary            Wet             0.50\n",
      "27  Season_binary       Slippery             0.50\n",
      "51           Rain            Wet             0.00\n",
      "57           Rain       Slippery             0.00\n",
      "3       Sprinkler  Season_binary             0.00\n",
      "9       Sprinkler           Rain             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "10 Largest Natural Direct Effects\n",
      " (direct influences only):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "43            Wet       Slippery             1.00\n",
      "7       Sprinkler            Wet             1.00\n",
      "16  Season_binary      Sprinkler             0.50\n",
      "52           Rain            Wet             0.00\n",
      "4       Sprinkler  Season_binary             0.00\n",
      "10      Sprinkler           Rain             0.00\n",
      "13      Sprinkler       Slippery             0.00\n",
      "22  Season_binary            Wet             0.00\n",
      "25  Season_binary           Rain             0.00\n",
      "28  Season_binary       Slippery             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "10 Largest Natural Indirect Effects\n",
      " (indirect influences only):\n",
      "        Treatment        Outcome Estimated_effect\n",
      "14      Sprinkler       Slippery             1.00\n",
      "59           Rain       Slippery             0.00\n",
      "5       Sprinkler  Season_binary             0.00\n",
      "8       Sprinkler            Wet             0.00\n",
      "11      Sprinkler           Rain             0.00\n",
      "17  Season_binary      Sprinkler             0.00\n",
      "23  Season_binary            Wet             0.00\n",
      "26  Season_binary           Rain             0.00\n",
      "29  Season_binary       Slippery             0.00\n",
      "32            Wet      Sprinkler             0.00\n",
      "\n",
      "================================\n",
      "\n",
      "Only quantitative estimates are shown. For methodic details, use Estimator.show_quick_result_methods.\n",
      "\n",
      "Average treatment effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                        \\\n",
      "Outcome                   Rain Season_binary      Slippery Sprinkler   \n",
      "Treatment                                                              \n",
      "Rain                       1.0           0.0  4.218847e-15  0.000000   \n",
      "Season_binary              0.0           1.0  4.990090e-01  0.499009   \n",
      "Slippery                   0.0           0.0  1.000000e+00  0.000000   \n",
      "Sprinkler                  0.0           0.0  1.000000e+00  1.000000   \n",
      "Wet                        0.0           0.0  1.000000e+00  0.000000   \n",
      "\n",
      "                             \n",
      "Outcome                 Wet  \n",
      "Treatment                    \n",
      "Rain           4.218847e-15  \n",
      "Season_binary  4.990090e-01  \n",
      "Slippery       0.000000e+00  \n",
      "Sprinkler      1.000000e+00  \n",
      "Wet            1.000000e+00  \n",
      "\n",
      "================================\n",
      "\n",
      "Natural direct effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                               \n",
      "Outcome                   Rain Season_binary Slippery Sprinkler           Wet\n",
      "Treatment                                                                    \n",
      "Rain                       1.0           0.0      0.0  0.000000  4.218847e-15\n",
      "Season_binary              0.0           1.0      0.0  0.499009  0.000000e+00\n",
      "Slippery                   0.0           0.0      1.0  0.000000  0.000000e+00\n",
      "Sprinkler                  0.0           0.0      0.0  1.000000  1.000000e+00\n",
      "Wet                        0.0           0.0      1.0  0.000000  1.000000e+00\n",
      "\n",
      "================================\n",
      "\n",
      "Natural indirect effects from quick analyses:\n",
      "\n",
      "              Estimated_effect                                           \n",
      "Outcome                   Rain Season_binary      Slippery Sprinkler  Wet\n",
      "Treatment                                                                \n",
      "Rain                       0.0           0.0  2.916287e-15       0.0  0.0\n",
      "Season_binary              0.0           0.0  0.000000e+00       0.0  0.0\n",
      "Slippery                   0.0           0.0  0.000000e+00       0.0  0.0\n",
      "Sprinkler                  0.0           0.0  1.000000e+00       0.0  0.0\n",
      "Wet                        0.0           0.0  0.000000e+00       0.0  0.0\n",
      "Saving all causal estimates from quick analyses to csv.\n",
      "\n",
      "Successfully generated report in c:\\Users\\D.Gruenbaum\\Desktop\\cause2e_bugfix\\cause2e\\output\\sprinkler\\sprinkler_report.pdf.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learner.data = learner.data[learner.data['Rain']==0]\n",
    "learner.run_all_quick_analyses()"
   ]
  }
 ],
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