Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25870

Where to weight? Estimating population causal effects with weighted double score matching in complex surveys

Yukang Zeng·Guangyu Tong·Jiaqi Tong·Haidong Lu·Bhramar Mukherjee·Fan Li
View original source ↗Read source document ↗Export metadata ↓

Abstract

Double score matching is an emerging method for causal inference that combines propensity and prognostic scores to achieve dimension reduction and model robustness. However, when the observational data are drawn from a complex survey, the existing literature does not provide sufficient guidance on the proper use of survey weights in matching strategies for population-level causal inference. We develop weighted double score matching (WDSM) that provides a unified framework for incorporating survey weights throughout the score estimation, matching, and outcome analysis under both retrospective (informative) and prospective (non-informative) sampling designs. We establish asymptotic normality of the resultant effect estimates under both known and estimated scores and develop a linearization-based bootstrap procedure for variance estimation that preserves the survey-weighted matching structure. Simulation studies show that WDSM substantially reduces bias relative to unweighted matching when either the propensity score model or the prognostic score models are correctly specified. Under correct specification, WDSM also improves efficiency and confidence interval coverage relative to survey-weighted propensity score matching, with particularly pronounced gains under poor overlap. Applications to early marijuana use and mental health in the National Survey on Drug Use and Health and to sleep duration and glycemic control in the National Health and Nutrition Examination Survey illustrate the practical value of WDSM for population-level inference. We conclude with a discussion of our findings and provide practical guidance on incorporating survey weights into matching-based analyses of observational data.

Explore related subjects

stat.ME

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yukang Zeng, Guangyu Tong, Jiaqi Tong, Haidong Lu, Bhramar Mukherjee, Fan Li. 2026-10-19. Where to weight? Estimating population causal effects with weighted double score matching in complex surveys. https://arxiv.org/abs/2610.25870

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator

Randomized experiments (or A/B tests) are widely used to evaluate interventions in dynamic systems such as recommendation platforms, marketplaces, and digital health. In these settings, interventions affect both current and future system states, so estimating the global average treatment effect (GATE) requires accounting for temporal dynamics, which is especially challenging in the presence of nonstationarity; existing approaches suffer from high bias, high variance, or both. In this paper, we address this challenge via the novel Truncated Policy Gradient (TPG) estimator, which replaces instantaneous outcomes with short-horizon outcome trajectories. The estimator admits a policy gradient interpretation: it is a truncation of the first-order approximation to the GATE, yielding explicit bias and variance bounds in nonstationary Markovian settings. For a fixed truncation size, we establish a central limit theorem and, under an additional mean-stability condition, the consistency of a variance estimator for nonstationary single-trajectory data. We validate our theory with two real-world case studies. The results show that relative to existing approaches, a well-calibrated TPG estimator can achieve a favorable balance between bias and variance in nonstationary settings, highlighting the value of the policy-gradient perspective for designing effective estimators under complex dynamics.

stat.ME↗
arXiv2026

Weak Factor Analysis via Temporally Weighted PCA

Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the noise has complex dependence structure. We argue that the inconsistency often stems from bias and introduce a general approach to restore consistency. Specifically, we propose a general weighting scheme for PCA and show that suitable weights can restore consistency for much weaker factors under the dependent-noise structures allowed by our model. Under the stronger signal-to-noise conditions needed for inference, we also establish asymptotic normality of the loading and factor estimators. While an oracle weighting matrix may require knowledge about the factors and covariance of the idiosyncratic noise that are not known a priori, we develop an agnostic approach to adaptively choose from a large class of weighting matrices that can be viewed as PCA for weighted linear combinations of auto-covariances among the observations. Theoretical and numerical results demonstrate the merits of our methodology over the usual PCA and other recently developed techniques for large-dimensional approximate factor models.

stat.ME↗
arXiv2026

Combining e-values using demi-supermartingales

We present a new method for combining e-variables through demi-supermartingales, which settles an old conjecture in the literature on nonparametric mean testing. It also provides an explicit concentration bound for a certain Kullback--Leibler-type statistic arising in the stochastic multi-armed bandit literature. All of these combination results hold for independent e-variables as well as for the class of co-valid e-variables, whose dependence structure lies somewhere between independence and sequential validity. The results are further generalized to compound e-variables. The proofs proceed by analyzing elementary symmetric polynomials and their behavior as nonnegative demi-supermartingales.

stat.ME↗