Primary Submission Category: Causal Inference and SUTVA/Consistencies Violations
Casual Inference in the Presence of Limited Overlap
Authors: Michael Elliott, Tingting Zhou, Rod Little,
Presenting Author: Michael Elliott*
Propensity score (PS) based methods are often used to control for observed confounders in observational studies of causal effects. For PS methods to work reliably, there should be sufficient overlap in the propensity score distributions between treatment groups. Limited overlap can result in fewer subjects being matched or in extreme weights causing numerical instability and bias in causal estimation. The problem of limited overlap suggests (a) defining alternative estimands that restrict inferences to subpopulations where all treatments have the potential to be assigned, and/or (b) excluding or down-weighting sample cases where the propensity to receive one of the compared treatments is close to zero. We compared several PS methods for estimation of alternative causal estimands when limited overlap occurs. Simulations suggest that, when there are extreme weights, penalized spline of propensity prediction that we recently developed tends to outperform the weighted estimators for ATE and performs similarly to the weighted estimators for alternative estimands. We illustrate with an example that assess whether right heart catheterization (RHC) is a beneficial treatment in treating critically ill patients.