Valid Inference After Causal Discovery
Paula Gradu, Tijana Zrnic, Zhaoran Wang, Michael I. Jordan · Journal of the American Statistical Association · 2024
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.