Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs

Causal discovery is crucial for causal inference in observational studies: 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 novel nonparametric local discovery algorithm that is tailored for downstream inference tasks while avoiding the pretreatment assumption. LDP is a constraint-based procedure that partitions …

case causal inference complexity cs.lg discovery global identification inference partitioning polynomial sample stat.me stat.ml studies unbiased