# Rate doubly robust estimation for weighted average treatment effects

arXiv: https://arxiv.org/abs/2509.14502 · HTML: https://mlsanity.com/paper/2509.14502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 95 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [Balancing Covariates via Propensity Score Weighting](https://mlsanity.com/paper/1609.07494/) (2016)
- [Doubly Robust Calibration of Prediction Sets under Covariate Shift](https://mlsanity.com/paper/2203.01761/) (2022)
- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation](https://mlsanity.com/paper/2005.10579/) (2020)
- [Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects](https://mlsanity.com/paper/2112.09313/) (2021)
- [Robust Inference Using Inverse Probability Weighting](https://mlsanity.com/paper/1810.11397/) (2018)
- [Data thinning for convolution-closed distributions](https://mlsanity.com/paper/2301.07276/) (2023)
- [Multi-Source Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2405.09331/) (2024)
- [Propensity score weighting under limited overlap and model misspecification](https://mlsanity.com/paper/2006.04038/) (2020)
- [Improving trial generalizability using observational studies](https://mlsanity.com/paper/2003.01242/) (2020)
- [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022)
- [Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments](https://mlsanity.com/paper/1508.06948/) (2015)
- [On Adaptive Propensity Score Truncation in Causal Inference](https://mlsanity.com/paper/1707.05861/) (2017)
- [Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study](https://mlsanity.com/paper/2204.10969/) (2022)
- [Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population](https://mlsanity.com/paper/2401.14512/) (2024)
- [A subsampled double bootstrap for massive data](https://mlsanity.com/paper/1508.01126/) (2015)
- [Propensity Score Weighting for Causal Subgroup Analysis](https://mlsanity.com/paper/2010.02121/) (2020)
- [PSweight: An R Package for Propensity Score Weighting Analysis](https://mlsanity.com/paper/2010.08893/) (2020)
- [Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-observations](https://mlsanity.com/paper/2103.00605/) (2021)
- [Overlap, matching, or entropy weights: what are we weighting for?](https://mlsanity.com/paper/2210.12968/) (2022)
- [Average treatment effect on the treated, under lack of positivity](https://mlsanity.com/paper/2309.01334/) (2023)
- [Heavy Tail Robust Estimation and Inference for Average Treatment Effects](https://mlsanity.com/paper/2412.08458/) (2024)
- [Variance estimation for the average treatment effects on the treated and on the controls](https://mlsanity.com/paper/2209.10742/) (2022)
- [Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times](https://mlsanity.com/paper/2304.00231/) (2023)
- [Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding](https://mlsanity.com/paper/2309.07273/) (2023)
- [A Unified Framework for Causal Estimand Selection](https://mlsanity.com/paper/2410.12093/) (2024)
- [COADVISE: Covariate Adjustment with Variable Selection in Randomized Controlled Trials](https://mlsanity.com/paper/2501.08945/) (2025)
- [Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials](https://mlsanity.com/paper/2501.18798/) (2025)
- [Integrating Randomized Controlled Trial and External Control Data Using Balancing Weights: A Comparison of Estimands and Estimators](https://mlsanity.com/paper/2502.13871/) (2025)
- [Variance Estimation for Weighted Average Treatment Effects](https://mlsanity.com/paper/2508.08167/) (2025)

## Abstract (arXiv)

The weighted average treatment effect (WATE) defines a versatile class of causal estimands for populations characterized by propensity score weights, including the average treatment effect (ATE), treatment effect on the treated (ATT), on controls (ATC), and for the overlap population (ATO). WATE has broad applicability in social and medical research, as many datasets from these fields align with its framework. However, the literature lacks a systematic investigation into the robustness and efficiency conditions for WATE estimation. Although doubly robust (DR) estimators are well-studied for ATE, their applicability to other WATEs remains uncertain. This paper investigates whether widely used WATEs admit DR or rate doubly robust (RDR) estimators and assesses the role of nuisance function accuracy, particularly with machine learning. Using semiparametric efficient influence function (EIF) theory and double/debiased machine learning (DML), we propose three RDR estimators under specific rate and regularity conditions and evaluate their performance via Monte Carlo simulations. Applications to NHANES data on smoking and blood lead levels, and SIPP data on 401(k) eligibility, demonstrate the methods' practical relevance in medical and social sciences.