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Estimation of causal effects of binary treatments in unconfounded studies
Author(s) -
Gutman Roee,
Rubin Donald B.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6532
Subject(s) - covariate , missing data , covariance , statistics , causal inference , analysis of covariance , imputation (statistics) , binary data , matching (statistics) , weighting , population , econometrics , outcome (game theory) , computer science , inverse probability weighting , mathematics , propensity score matching , binary number , medicine , arithmetic , environmental health , mathematical economics , radiology
Estimation of causal effects in non‐randomized studies comprises two distinct phases: design, without outcome data, and analysis of the outcome data according to a specified protocol. Recently, Gutman and Rubin (2013) proposed a new analysis‐phase method for estimating treatment effects when the outcome is binary and there is only one covariate, which viewed causal effect estimation explicitly as a missing data problem. Here, we extend this method to situations with continuous outcomes and multiple covariates and compare it with other commonly used methods (such as matching, subclassification, weighting, and covariance adjustment). We show, using an extensive simulation, that of all methods considered, and in many of the experimental conditions examined, our new ‘multiple‐imputation using two subclassification splines’ method appears to be the most efficient and has coverage levels that are closest to nominal. In addition, it can estimate finite population average causal effects as well as non‐linear causal estimands. This type of analysis also allows the identification of subgroups of units for which the effect appears to be especially beneficial or harmful. Copyright © 2015 John Wiley & Sons, Ltd.