IHDP 데이터셋에 DoWhy 적용하기

가정 방문과 특수 아동 발달 센터 방문이 조산아의 건강 및 발달에 얼마나 영향을 줄까?

작성자: 허현
원문: DoWhy example on ihdp (Infant Health and Development Program) dataset

역자주 - 원문의 내용 자체가 IHDP 데이터셋에 대한 설명이나 인과분석 과정에 대한 설명이 생략되어 있습니다. 해당 문서의 주요 목적은 여러 Propensity Score 방법론과 Refute 방법론을 사용하는 코드 레퍼런스를 남기는 것에 있기 때문에 관련 내용에 대한 학습이 필요한 경우 DOWHY KEY CONCEPTS의 성향점수(Propensity Score)와 추정치를 검증하는 방법를 참고하시면 좋습니다.

# importing required libraries : 필요 라이브러리 불러오기
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np

데이터 로드

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
0	True	5.599916	4.318780	3.268256	6.854457	-0.528603	-0.343455	1.128554	0.161703	-0.316603	...	1	1	1	1
1	False	6.875856	7.856495	6.636059	7.562718	-1.736945	-1.802002	0.383828	2.244320	-0.629189	...	1	1	1	1
2	False	2.996273	6.633952	1.570536	6.121617	-0.807451	-0.202946	-0.360898	-0.879606	0.808706	...	1	0	1	1
3	False	1.366206	5.697239	1.244738	5.889125	0.390083	0.596582	-1.850350	-0.879606	-0.004017	...	1	0	1	1
4	False	1.963538	6.202582	1.685048	6.191994	-1.045229	-0.602710	0.011465	0.161703	0.683672	...	1	1	1	1

5 rows × 30 columns

1. Model

# Create a causal model from the data and given common causes. : 데이터와 공통 원인으로 인과 모델 생성
xs = ""
for i in range(1,26):
    xs += ("x"+str(i)+"+")

model=CausalModel(
        data = data,
        treatment='treatment',
        outcome='y_factual',
        common_causes=xs.split('+')
        )

2. Identify

#Identify the causal effect : 인과 효과 규명하기
identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
print(identified_estimand)

Estimand type: nonparametric-ate

### Estimand : 1
Estimand name: backdoor1 (Default)
Estimand expression:
     d
────────────(Expectation(y_factual|x18,x17,x11,x2,x5,x14,x22,x23,x24,x21,x16,x20,x8,x4,x7,x19,x10,x15,x25,x9,x12,x3,x6,x1,x13))

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

3. Estimate (using different methods)

3.1 Using Linear Regression

# Estimate the causal effect and compare it with Average Treatment Effect : 인과 효과를 추정하고 ATE와 비교
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: nonparametric-ate

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

## Estimate
Mean value: 3.928671750872714
p-value: [1.58915682e-156]

Causal Estimate is 3.928671750872714
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 3.9791388232170393
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 3.409737824407883
ATE 4.021121012430829

4. Refute

4.1 random_common_cause

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

Refute: Add a Random Common Cause
Estimated effect:3.409737824407883
New effect:3.4652727093798434

4.2 placebo_treatment_refuter

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:3.409737824407883
New effect:-0.023837129277084368
p value:0.44999999999999996

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:3.409737824407883
New effect:3.3635040963705256
p value:0.29000000000000004

Previous호텔 예약 취소에 숨겨진 인과적 이야기 Next사용자 정의 결과 함수를 사용해 추정치 반박하기

Last updated 2 years ago