Balancing Weights for Causal Inference in Observational Factorial Studies

• 成果类型: Article; Early Access
• 署名作者: Yu, Ruoqi; Ding, Peng
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2602837
• 发表日期: 2026-03-11
• 关键词: covariate balance; factorial design; Multiple factors; Observational Study; Weighting; volatile organic-compounds; propensity score; multiple treatments; designs; RERANDOMIZATION; association; adjustment; exposure

摘要

Many scientific questions in biomedical, environmental, and psychological research involve understanding the effects of multiple factors on outcomes. While factorial experiments are ideal for this purpose, randomized controlled treatment assignment is generally infeasible in many empirical studies. Therefore, investigators must rely on observational data, where drawing reliable causal inferences for multiple factors remains challenging. As the number of treatment combinations grows exponentially with the number of factors, some treatment combinations can be rare or missing by chance in observed data, further complicating factorial effects estimation. To address these challenges, we propose a novel weighting method tailored to observational studies with multiple factors. Our approach uses weighted observational data to emulate a randomized factorial experiment, enabling simultaneous estimation of the effects of multiple factors and their interactions. Our investigations reveal a crucial nuance: achieving balance among covariates, as in single-factor scenarios, is necessary but insufficient for unbiasedly estimating factorial effects; balancing the factors is also essential in multi-factor settings. Moreover, we extend our weighting method to handle missing treatment combinations in observed data. Finally, we study the asymptotic behavior of the new weighting estimators and propose a consistent variance estimator, providing reliable inferences on factorial effects in observational studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.