DoWhy example on ihdp (Infant Health and Development Program) dataset

# importing required libraries
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np

Loading Data

data= pd.read_csv("https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/master/datasets/IHDP/csv/ihdp_npci_1.csv", header = None)
col =  ["treatment", "y_factual", "y_cfactual", "mu0", "mu1" ,]
for i in range(1,26):
    col.append("x"+str(i))
data.columns = col
data = data.astype({"treatment":'bool'}, copy=False)
data.head()

	treatment	y_factual	y_cfactual	mu0	mu1	x1	x2	x3	x4	x5	...	x16	x17	x18	x19	x20	x21	x22	x23	x24	x25
0	True	5.599916	4.318780	3.268256	6.854457	-0.528603	-0.343455	1.128554	0.161703	-0.316603	...	1	1	1	1	0	0	0	0	0	0
1	False	6.875856	7.856495	6.636059	7.562718	-1.736945	-1.802002	0.383828	2.244320	-0.629189	...	1	1	1	1	0	0	0	0	0	0
2	False	2.996273	6.633952	1.570536	6.121617	-0.807451	-0.202946	-0.360898	-0.879606	0.808706	...	1	0	1	1	0	0	0	0	0	0
3	False	1.366206	5.697239	1.244738	5.889125	0.390083	0.596582	-1.850350	-0.879606	-0.004017	...	1	0	1	1	0	0	0	0	0	0
4	False	1.963538	6.202582	1.685048	6.191994	-1.045229	-0.602710	0.011465	0.161703	0.683672	...	1	1	1	1	0	0	0	0	0	0

5 rows × 30 columns

1.Model

# Create a causal model from the data and given common causes.
model=CausalModel(
        data = data,
        treatment='treatment',
        outcome='y_factual',
        common_causes=["x"+str(i) for  i in range(1,26)]
        )
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))

2.Identify

#Identify the causal effect
identified_estimand = model.identify_effect(proceed_when_unidentifiable=True, method_name="maximal-adjustment")
print(identified_estimand)

Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
     d
────────────(E[y_factual|x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1
d[treatment]


,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14])

Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14,U) = P(y_factual|treatment,x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14)

### Estimand : 2
Estimand name: iv
No such variable(s) found!

### Estimand : 3
Estimand name: frontdoor
No such variable(s) found!

3. Estimate (using different methods)

3.1 Using Linear Regression

# Estimate the causal effect and compare it with Average Treatment Effect
estimate = model.estimate_effect(identified_estimand,
        method_name="backdoor.linear_regression", test_significance=True
)

print(estimate)

print("Causal Estimate is " + str(estimate.value))
data_1 = data[data["treatment"]==1]
data_0 = data[data["treatment"]==0]

print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))

*** Causal Estimate ***

## Identified estimand
Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
     d
────────────(E[y_factual|x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1
d[treatment]


,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14])

Estimand assumption 1, Unconfoundedness: If U→{treatment} and U→y_factual then P(y_factual|treatment,x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14,U) = P(y_factual|treatment,x15,x10,x17,x24,x11,x16,x12,x21,x6,x3,x19,x4,x9,x5,x1,x7,x23,x20,x2,x13,x22,x8,x25,x18,x14)

## Realized estimand
b: y_factual~treatment+x15+x10+x17+x24+x11+x16+x12+x21+x6+x3+x19+x4+x9+x5+x1+x7+x23+x20+x2+x13+x22+x8+x25+x18+x14
Target units: ate

## Estimate
Mean value: 3.9286717508727196
p-value: [1.58915682e-156]

Causal Estimate is 3.9286717508727196
ATE 4.021121012430829

3.2 Using Propensity Score Matching

estimate = model.estimate_effect(identified_estimand,
        method_name="backdoor.propensity_score_matching"
)

print("Causal Estimate is " + str(estimate.value))

print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))

Causal Estimate is 3.97913882321704
ATE 4.021121012430829

3.3 Using Propensity Score Stratification

estimate = model.estimate_effect(identified_estimand,
        method_name="backdoor.propensity_score_stratification", method_params={'num_strata':50, 'clipping_threshold':5}
)

print("Causal Estimate is " + str(estimate.value))
print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))

Causal Estimate is 3.4550471588628207
ATE 4.021121012430829

3.4 Using Propensity Score Weighting

estimate = model.estimate_effect(identified_estimand,
        method_name="backdoor.propensity_score_weighting"
)

print("Causal Estimate is " + str(estimate.value))

print("ATE", np.mean(data_1["y_factual"])- np.mean(data_0["y_factual"]))

Causal Estimate is 4.028748218389785
ATE 4.021121012430829

4. Refute

refute_results=model.refute_estimate(identified_estimand, estimate,
        method_name="random_common_cause")
print(refute_results)

Refute: Add a random common cause
Estimated effect:4.028748218389785
New effect:4.0287482183897865
p value:1.0

res_placebo=model.refute_estimate(identified_estimand, estimate,
        method_name="placebo_treatment_refuter", placebo_type="permute")
print(res_placebo)

Refute: Use a Placebo Treatment
Estimated effect:4.028748218389785
New effect:-0.45938849114748315
p value:0.06

4.3 Data Subset Refuter

res_subset=model.refute_estimate(identified_estimand, estimate,
        method_name="data_subset_refuter", subset_fraction=0.9)
print(res_subset)

Refute: Use a subset of data
Estimated effect:4.028748218389785
New effect:4.027501315356422
p value:0.98