Valid Inference After Causal Discovery
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryCausal InferencevalidSimilar Papers 제목 키워드 기반
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Causal discovery is crucial for causal inference in observational studies, as it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation. However, global causal discovery is notoriousl…
Causal DiscoveryCausal InferencevalidTransforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration
We introduce a novel framework for temporal causal discovery and inference that addresses two key challenges: complex nonlinear dependencies and spurious correlations. Our approach employs a multi-layer Transformer-based…
Local Causal Discovery for Statistically Efficient Causal Inference
Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery methods focus on learning…
Computational EfficiencyCausal InferenceThe Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are not usually available. Furthermore, obs…
Causal DiscoveryCausal InferenceDecision MakingMissing ValuesFrom Causal Discovery to Dynamic Causal Inference in Neural Time Series
Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied i…
Causal Inference