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

[1]:
# importing required libraries
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np

Loading Data

[2]:
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()
[2]:
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

[3]:
# 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"))
../_images/example_notebooks_dowhy_ihdp_data_example_5_0.png
../_images/example_notebooks_dowhy_ihdp_data_example_5_1.png

2.Identify

[4]:
#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|x17,x8,x9,x25,x12,x4,x14,x11,x1,x16,x22,x15,x19,x18,x
d[treatment]


21,x13,x10,x20,x3,x23,x24,x7,x2,x6,x5])

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

### 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

[5]:
# 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|x17,x8,x9,x25,x12,x4,x14,x11,x1,x16,x22,x15,x19,x18,x
d[treatment]


21,x13,x10,x20,x3,x23,x24,x7,x2,x6,x5])

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

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

## Estimate
Mean value: 3.9286717508727147
p-value: [1.58915682e-156]

Causal Estimate is 3.9286717508727147
ATE 4.021121012430829

3.2 Using Propensity Score Matching

[6]:
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

[7]:
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

[8]:
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 4.028748218389734
ATE 4.021121012430829

4. Refute

[9]:
refute_results=model.refute_estimate(identified_estimand, estimate,
        method_name="random_common_cause")
print(refute_results)
Refute: Add a random common cause
Estimated effect:4.028748218389734
New effect:4.028748218389736
p value:1.0

[10]:
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:4.028748218389734
New effect:-0.4715131738219758
p value:0.02

4.3 Data Subset Refuter

[11]:
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:4.028748218389734
New effect:4.023429640535523
p value:0.96