Local Causal Discovery for Estimating Causal Effects

arXiv:2302.08070v4 Announce Type: replace
Abstract: Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can identify this class under strong faithfulness assumptions, it can be computationally prohibitive. Fortunately, only the local graph structure around …

abstract arxiv causal class cs.lg data discovery effects graph markov narrow stat.me the graph treatment type values