One-step TMLE for weighted average treatment effects
Abstract
We consider Targeted Maximum Likelihood Estimation (TMLE) of weighted average treatment effects (WATEs), a class of causal estimands that reweight the covariate distribution using a specified function of the propensity score. This class includes the average treatment effect and average treatment effect on the treated, as well as various overlap-based targets. We provide a comprehensive analysis of the one-step TMLE along the universal least favorable path for such parameters. Under explicit regularity conditions on the weight function and initialization, we show that the targeting procedure is well-defined, reaches a solution of the estimating equation in finite time, and yields an asymptotically efficient estimator. In particular, convergence of the targeting dynamics and control of the second-order remainder are derived from these conditions rather than imposed as separate assumptions on the output of the algorithm.
Keywords
Cite
@article{arxiv.2604.00198,
title = {One-step TMLE for weighted average treatment effects},
author = {Yang Liu and Patrick Lopatto and Ivana Malenica},
journal= {arXiv preprint arXiv:2604.00198},
year = {2026}
}