Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

Jing Qin, Tao Yu, Pengfei Li, Hao Liu, Baojiang Chen

Research output: Contribution to journalArticle

Abstract

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.

Original languageEnglish (US)
Pages (from-to)1442-1458
Number of pages17
JournalStatistics in Medicine
Volume38
Issue number8
DOIs
StatePublished - Apr 15 2019

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Keywords

  • causal inference
  • empirical process
  • inverse weighting
  • missing data
  • pool adjacent violation algorithm
  • single-index model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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