Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching
This work addresses model misspecification and positivity violations in causal inference for researchers, though it appears incremental as it builds on existing matching and weighting techniques.
The paper tackles the problem of estimating various causal effects in causal inference by introducing the generalized average treatment effect (GATE) to unify existing estimands and developing a Kernel Optimal Matching (KOM) method for optimal estimation, with evaluation in simulations and case studies on spine surgery and HIV peer support.
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad-hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regression modeling, but these may be sensitive to model misspecification, practical violations of positivity, or both. The contribution of this paper is twofold. First, we formulate the generalized average treatment effect (GATE) to unify these causal estimands as well as their IPW estimates. Second, we develop a method based on Kernel Optimal Matching (KOM) to optimally estimate GATE and to find the GATE most easily estimable by KOM, which we term the Kernel Optimal Weighted Average Treatment Effect. KOM provides uniform control on the conditional mean squared error of a weighted estimator over a class of models while simultaneously controlling for precision. We study its theoretical properties and evaluate its comparative performance in a simulation study. We illustrate the use of KOM for GATE estimation in two case studies: comparing spine surgical interventions and studying the effect of peer support on people living with HIV.