On Adaptive Propensity Score Truncation in Causal Inference

Abstract

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.

References in corpus (3)

• The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
• Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
• Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods

Cited by in corpus (2)

• More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions
• Collaborative targeted inference from continuously indexed nuisance parameter estimators