Valid Inference After Causal Discovery

• 成果类型: Article
• 署名作者: Gradu, Paula; Zrnic, Tijana; Wang, Yixin; Jordan, Michael I.
• 署名单位: University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2402089
• 发表日期: 2025
• 页码: 1127-1138
• 关键词: Post-selection Inference; models

摘要

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to double dipping, invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while allowing for a trade-off between causal discovery accuracy and confidence interval width. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.