Title

Facilitating Score and Causal Inference Trees for Large Observational Studies

Authors

Authors

X. G. Su; J. Kang; J. J. Fan; R. A. Levine;X. Yan

Comments

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Abbreviated Journal Title

J. Mach. Learn. Res.

Keywords

CART; causal inference; confounding; interaction; observational study; personalized medicine; recursive partitioning; CONSISTENT NONPARAMETRIC REGRESSION; RECURSIVE PARTITIONING SCHEMES; PROPENSITY SCORE; DIMENSION REDUCTION; TRAINING-PROGRAMS; G-COMPUTATION; DATA SET; IDENTIFICATION; MODEL; STRATIFICATION; Automation & Control Systems; Computer Science, Artificial Intelligence

Abstract

Assessing treatment effects in observational studies is a multifaceted problem that not only involves heterogeneous mechanisms of how the treatment or cause is exposed to subjects, known as propensity, but also differential causal effects across sub-populations. We introduce a concept termed the facilitating score to account for both the confounding and interacting impacts of covariates on the treatment effect. Several approaches for estimating the facilitating score are discussed. In particular, we put forward a machine learning method, called causal inference tree (CIT), to provide a piecewise constant approximation of the facilitating score. With interpretable rules, CIT splits data in such a way that both the propensity and the treatment effect become more homogeneous within each resultant partition. Causal inference at different levels can be made on the basis of CIT. Together with an aggregated grouping procedure, CIT stratifies data into strata where causal effects can be conveniently assessed within each. Besides, a feasible way of predicting individual causal effects (ICE) is made available by aggregating ensemble CIT models. Both the stratified results and the estimated ICE provide an assessment of heterogeneity of causal effects and can be integrated for estimating the average causal effect (ACE). Mean square consistency of CIT is also established. We evaluate the performance of proposed methods with simulations and illustrate their use with the NSW data in Dehejia and Wahba (1999) where the objective is to assess the impact of a labor training program, the National Supported Work (NSW) demonstration, on post-intervention earnings.

Journal Title

Journal of Machine Learning Research

Volume

13

Publication Date

1-1-2012

Document Type

Article

Language

English

First Page

2955

Last Page

2994

WOS Identifier

WOS:000313200000005

ISSN

1532-4435

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