Augmented Weighted Estimators Dealing with Practical Positivity Violation to Causal inferences in a Random Coefficient Model
Mary Ying-Fang Wang (),
Paul Tuss and
Lihong Qi ()
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Mary Ying-Fang Wang: California State University, Center for Teacher Quality
Paul Tuss: California State University, Educator Quality Center
Lihong Qi: University of California, Davis
Psychometrika, 2019, vol. 84, issue 2, No 6, 447-467
Abstract The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study.
Keywords: experimental treatment assignment assumption; common support; endogeneity; hierarchical linear model; multilevel model; value added analysis (search for similar items in EconPapers)
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