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May 2018 Causal Inference: A Missing Data Perspective
Peng Ding, Fan Li
Statist. Sci. 33(2): 214-237 (May 2018). DOI: 10.1214/18-STS645

Abstract

Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential outcomes is observed and the rest are missing, causal inference is inherently a missing data problem. Indeed, there is a close analogy in the terminology and the inferential framework between causal inference and missing data. Despite the intrinsic connection between the two subjects, statistical analyses of causal inference and missing data also have marked differences in aims, settings and methods. This article provides a systematic review of causal inference from the missing data perspective. Focusing on ignorable treatment assignment mechanisms, we discuss a wide range of causal inference methods that have analogues in missing data analysis, such as imputation, inverse probability weighting and doubly robust methods. Under each of the three modes of inference—Frequentist, Bayesian and Fisherian randomization—we present the general structure of inference for both finite-sample and super-population estimands, and illustrate via specific examples. We identify open questions to motivate more research to bridge the two fields.

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Peng Ding. Fan Li. "Causal Inference: A Missing Data Perspective." Statist. Sci. 33 (2) 214 - 237, May 2018. https://doi.org/10.1214/18-STS645

Information

Published: May 2018
First available in Project Euclid: 3 May 2018

zbMATH: 1397.62125
MathSciNet: MR3797711
Digital Object Identifier: 10.1214/18-STS645

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.33 • No. 2 • May 2018
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