Simultaneous Estimation of Multiple Treatment Effects from Observational Studies

Unmeasured confounding presents a significant challenge in causal inference from observational studies. Classical approaches often rely on collecting proxy variables, such as instrumental variables. However, in applications where the effects of multiple treatments are of simultaneous interest, finding a sufficient number of proxy variables for consistent estimation of treatment effects can be challenging. Various methods in the literature exploit the structure of multiple treatments to address unmeasured confounding. In this paper, we introduce a novel approach to causal inference with multiple treatments, assuming sparsity in the causal effects. Our procedure autonomously selects treatments with non-zero causal effects, thereby providing a sparse causal estimation. Comprehensive evaluations using both simulated and Genome-Wide Association Study (GWAS) datasets demonstrate the effectiveness and robustness of our method compared to alternative approaches.

未观测混杂（Unmeasured confounding）是基于观察性研究开展因果推断时面临的重大挑战。传统方法通常依赖代理变量（proxy variables），例如工具变量（instrumental variables）。然而，在同时关注多种处理（multiple treatments）效应的应用场景中，找到足够数量的代理变量以实现处理效应的一致估计往往颇具难度。现有文献中已有多种方法利用多处理的结构来解决未观测混杂问题。本文提出了一种面向多处理场景的因果推断新方法，该方法假设因果效应存在稀疏性。我们的方法可自主筛选出具有非零因果效应的处理，从而实现稀疏因果估计。通过模拟数据集与全基因组关联研究（Genome-Wide Association Study, GWAS）数据集开展的全面评估表明，相较于其他同类方法，本文所提方法具备更优异的有效性与鲁棒性。