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arXiv · 2604.00198

One-step TMLE for weighted average treatment effects

Yang Liu·Patrick Lopatto·Ivana Malenica
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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.

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Yang Liu, Patrick Lopatto, Ivana Malenica. 2026-03-31. One-step TMLE for weighted average treatment effects. https://arxiv.org/abs/2604.00198

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