Title: Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference
Authors: Tao Yu - National University of Singapore (Singapore) [presenting]
Jing Qin - National Institutes of Health (United States)
Pengfei Li - University of Waterloo (Canada)
Hao Liu - Baylor College of Medicine (United States)
Baojiang Chen - University of Texas Health Science Center at Houston -- Austin Regional Campus (United States)
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 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 augmented inverse weighting estimators 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.