Related papers: Valid Inference after Causal Discovery

Valid Inference after Causal Discovery

URL: http://arxiv.org/abs/2208.05949v2
Date: Tue, 21 Mar 2023 00:28:23 GMT
Title: Valid Inference after Causal Discovery
Authors: Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
Abstract summary: We develop tools for valid post-causal-discovery inference. We show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates.
Score: 95.20382312836541
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 achieving more accurate causal discovery than data splitting.

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