  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 requiring parametric and pretreatment assumptions. LDP is a
constraint-based procedure that returns a VAS for an exposure-outcome pair
under latent confounding, given sufficient conditions. The total number of
independence tests performed is worst-case quadratic with respect to the
cardinality of the variable set. Asymptotic theoretical guarantees are
numerically validated on synthetic graphs. Adjustment sets from LDP yield less
biased and more precise average treatment effect estimates than baseline
discovery algorithms, with LDP outperforming on confounder recall, runtime, and
test count for VAS discovery. Notably, LDP ran at least 1300x faster than
baselines on a benchmark.
