Suehyun Kim | Searcharxiv

arXiv2025

A Design-Based Matching Framework for Staggered Adoption with Time-Varying Confounding

Causal inference in longitudinal datasets has long been challenging due to dynamic treatment adoption and confounding by time-varying covariates. Prior work either fails to account for heterogeneity across treatment adoption cohorts and treatment timings or relies on modeling assumptions. In this paper, we develop a novel design-based framework for inference on group- and time-specific treatment effects in panel data with staggered treatment adoption. We establish identification results for causal effects under this structure and introduce corresponding estimators, together with a block bootstrap procedure for estimating the covariance matrix and testing the homogeneity of group-time treatment effects. To implement the framework in practice, we propose the Reverse-Time Nested Matching algorithm, which constructs matched strata by pairing units from different adoption cohorts in a way that ensures comparability of covariate histories at each treatment time. Applying the algorithm to the Netflix-IPTV dataset, we find that while Netflix subscription does not significantly affect total IPTV viewing time, it does negatively affect VoD usage. We also provide statistical evidence that the causal effects of Netflix subscription may vary even within the same treatment cohort or across the same outcome and event times.

stat.ME↗

arXiv2025

Mixing Samples to Address Weak Overlap in Causal Inference

In observational studies, the assumption of sufficient overlap (positivity) is fundamental for the identification and estimation of causal effects. Failing to account for this assumption yields inaccurate and potentially infeasible estimators. To address this issue, we introduce a simple yet novel approach, \textit{mixing}, which mitigates overlap violations by constructing a synthetic treated group that combines treated and control units. Our strategy offers three key advantages. First, it improves the accuracy of the estimator by preserving unbiasedness while reducing variance. The benefit is particularly significant in settings with weak overlap, though the method remains effective regardless of the overlap level. This phenomenon results from the shrinkage of propensity scores in the mixed sample, which enhances robustness to poor overlap. Second, it enables direct estimation of the target estimand without discarding extreme observations or modifying the target population, thus facilitating a straightforward interpretation of the results. Third, the mixing approach is highly adaptable to various weighting schemes, including contemporary methods such as entropy balancing. The estimation of the Mixed IPW (MIPW) estimator is done via M-estimation, and the method extends to a broader class of weighting estimators through a resampling algorithm. We illustrate the mixing approach through extensive simulation studies and provide practical guidance with a real-data analysis.

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