Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
通过分割实现暴露—结果对的多项式时间局部因果发现
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 notoriously hard in the nonparametric setting, with exponential time and sample complexity in the worst case. To address this, we propose local discovery by partitioning (LDP): a local causal discovery method that is tailored for downstream inference tasks without…