Add examples from "Causal Inference and Discovery in Python" to tests (…

…#1165)

* Add test file

Signed-off-by: Rahul Shrestha <rahulshrestha0101@gmail.com>

added chapter 6

Fix

Signed-off-by: Rahul Shrestha <rahulshrestha0101@gmail.com>

Add chapter 8

Signed-off-by: Rahul Shrestha <rahulshrestha0101@gmail.com>

Fix formatting issue with linter

Add references

* Added reference message

Signed-off-by: rahulbshrestha <rahulshrestha0101@gmail.com>

* Added econml requirement

Signed-off-by: rahulbshrestha <rahulshrestha0101@gmail.com>

* removed econml from imports

Signed-off-by: Amit Sharma <amit_sharma@live.com>

---------

Signed-off-by: rahulbshrestha <rahulshrestha0101@gmail.com>
Signed-off-by: Amit Sharma <amit_sharma@live.com>
Co-authored-by: Amit Sharma <amit_sharma@live.com>

tests/causal-inference-discovery-book/test_causal_inference_discovery_book.py

@@ -0,0 +1,199 @@
+"""
+Note: The tests below are taken from the book 'Causal Inference and Discovery in Python' (https://www.packtpub.com/product/causal-inference-and-discovery-in-python/9781804612989)
+by Aleksander Molak (https://github.com/AlxndrMlk). Other than the assert statements, all of the code below is taken from the book.
+"""
+
+import numpy as np
+import pandas as pd
+from pytest import mark
+from scipy import stats
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.linear_model import LassoCV, LinearRegression, LogisticRegression
+from tqdm import tqdm
+
+from dowhy import CausalModel
+
+
+class GPSMemorySCM:
+    def __init__(self, random_seed=None):
+        self.random_seed = random_seed
+        self.u_x = stats.truncnorm(0, np.infty, scale=5)
+        self.u_y = stats.norm(scale=2)
+        self.u_z = stats.norm(scale=2)
+        self.u = stats.truncnorm(0, np.infty, scale=4)
+
+    def sample(self, sample_size=100, treatment_value=None):
+        """Samples from the SCM"""
+        if self.random_seed:
+            np.random.seed(self.random_seed)
+
+        u_x = self.u_x.rvs(sample_size)
+        u_y = self.u_y.rvs(sample_size)
+        u_z = self.u_z.rvs(sample_size)
+        u = self.u.rvs(sample_size)
+
+        if treatment_value:
+            gps = np.array([treatment_value] * sample_size)
+        else:
+            gps = u_x + 0.7 * u
+
+        hippocampus = -0.6 * gps + 0.25 * u_z
+        memory = 0.7 * hippocampus + 0.25 * u
+
+        return gps, hippocampus, memory
+
+    def intervene(self, treatment_value, sample_size=100):
+        """Intervenes on the SCM"""
+        return self.sample(treatment_value=treatment_value, sample_size=sample_size)
+
+
+@mark.usefixtures("fixed_seed")
+class TestCausalInferenceDiscoveryBook(object):
+    def test_dowhy_chapter_8(self):
+        """
+        This test was taken from Chapter 8 of the book, 'Causal Inference and Discovery in Python'
+        """
+
+        # Construct the graph (the graph is constant for all iterations)
+        nodes = ["S", "Q", "X", "Y", "P"]
+        edges = ["SQ", "SY", "QX", "QY", "XP", "YP", "XY"]
+
+        # Generate the GML graph
+        gml_string = "graph [directed 1\n"
+
+        for node in nodes:
+            gml_string += f'\tnode [id "{node}" label "{node}"]\n'
+
+        for edge in edges:
+            gml_string += f'\tedge [source "{edge[0]}" target "{edge[1]}"]\n'
+
+        gml_string += "]"
+
+        # Define the true effect
+        TRUE_EFFECT = 0.7
+
+        # Define experiment params
+        sample_sizes = [30, 100, 1000, 10000]
+        noise_coefs = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
+        n_samples = 20
+
+        # Record the results
+        results = []
+
+        # Run the experiment
+        for sample_size in tqdm(sample_sizes):
+            for noise_coef in noise_coefs:
+                for i in range(n_samples):
+                    # Generate the data
+                    S = np.random.random(sample_size)
+                    Q = 0.2 * S + noise_coef * np.random.random(sample_size)
+                    X = 0.14 * Q + noise_coef * np.random.random(sample_size)
+                    Y = TRUE_EFFECT * X + 0.11 * Q + 0.32 * S + noise_coef * np.random.random(sample_size)
+                    P = 0.43 * X + 0.21 * Y + noise_coef * np.random.random(sample_size)
+
+                    # Encode as a pandas df
+                    df = pd.DataFrame(np.vstack([S, Q, X, Y, P]).T, columns=["S", "Q", "X", "Y", "P"])
+
+                    # Instantiate the CausalModel
+                    model = CausalModel(data=df, treatment="X", outcome="Y", graph=gml_string)
+
+                    # Get the estimand
+                    estimand = model.identify_effect()
+
+                    # Get estimate (DML)
+                    estimate_dml = model.estimate_effect(
+                        identified_estimand=estimand,
+                        method_name="backdoor.econml.dml.DML",
+                        method_params={
+                            "init_params": {
+                                "model_y": GradientBoostingRegressor(),
+                                "model_t": GradientBoostingRegressor(),
+                                "model_final": LassoCV(fit_intercept=False),
+                            },
+                            "fit_params": {},
+                        },
+                    )
+
+                    # Get estimate (Linear Regression)
+                    estimate_lr = model.estimate_effect(
+                        identified_estimand=estimand, method_name="backdoor.linear_regression"
+                    )
+
+                    results.append(
+                        {
+                            "sample_size": sample_size,
+                            "noise_coef": noise_coef,
+                            "estimate_dml": estimate_dml.value,
+                            "estimate_lr": estimate_lr.value,
+                            "error_dml": estimate_dml.value - TRUE_EFFECT,
+                            "error_lr": estimate_lr.value - TRUE_EFFECT,
+                        }
+                    )
+
+    def test_dowhy_chapter_7(self):
+        """
+        This test was taken from Chapter 7 of the book, 'Causal Inference and Discovery in Python'
+        """
+
+        # Instantiate the SCM
+        scm = GPSMemorySCM()
+        # Generate observational data
+        gps_obs, hippocampus_obs, memory_obs = scm.sample(1000)
+        # Encode as a pandas df
+        df = pd.DataFrame(np.vstack([gps_obs, hippocampus_obs, memory_obs]).T, columns=["X", "Z", "Y"])
+
+        # Create the graph describing the causal structure
+        gml_graph = """
+        graph [
+            directed 1
+            
+            node [
+                id "X" 
+                label "X"
+            ]    
+            node [
+                id "Z"
+                label "Z"
+            ]
+            node [
+                id "Y"
+                label "Y"
+            ]
+            node [
+                id "U"
+                label "U"
+            ]
+            
+            edge [
+                source "X"
+                target "Z"
+            ]
+            edge [
+                source "Z"
+                target "Y"
+            ]
+            edge [
+                source "U"
+                target "X"
+            ]
+            edge [
+                source "U"
+                target "Y"
+            ]
+        ]
+        """
+
+        # With graph
+        model = CausalModel(data=df, treatment="X", outcome="Y", graph=gml_graph)
+
+        # model.view_model()
+
+        estimand = model.identify_effect()
+
+        estimate = model.estimate_effect(identified_estimand=estimand, method_name="frontdoor.two_stage_regression")
+
+        refute_subset = model.refute_estimate(
+            estimand=estimand, estimate=estimate, method_name="data_subset_refuter", subset_fraction=0.4
+        )
+
+        assert refute_subset is not None