The synthetic instrument: from sparse association to sparse causation

• 成果类型: Article
• 署名作者: Tang, Dingke; Kong, Dehan; Wang, Linbo
• 署名单位: University of Ottawa; University of Toronto
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkaf083
• 发表日期: 2026-09
• 页码: 1210-1230
• 关键词: Causal Inference; multivariate analysis; unmeasured confounding; selection; regression; blessings; inference; subset; models

摘要

In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are sparse. We show that under sparse causation, causal effects are identifiable even with unmeasured confounding. Our proposal is built around a novel device called the synthetic instrument, which, in contrast to standard instrumental variables, can be constructed directly from the observed exposures. We demonstrate that, under the assumption of sparse causation, the problem of causal effect estimation can be formulated as an & ell;0-penalization problem and solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-the-art methods in both low- and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.