Less Greedy Equivalence Search
Adiba Ejaz, Elias Bareinboim
Greedy Equivalence Search (GES) is a classic score-based algorithm for causal discovery from observational data.In the sample limit, it recovers the Markov equivalence class of graphs that describe the data.Still, it faces two challenges in practice: computational cost and finite-sample accuracy.In this paper, we develop Less Greedy Equivalence Search (LGES), a variant of GES that retains its theoretical guarantees while partially addressing these limitations.LGES modifies the greedy step: rather than always applying the highest-scoring insertion, it avoids edge insertions between variables for which the score implies some conditional independence.This more targeted search yields up to a $10$-fold speed-up and $3$-fold reduction in structural error relative to GES.Moreover, LGES can guide the search using prior assumptions, while correcting these assumptions when contradicted by the data.Finally, LGES can exploit interventional data to refine the learned observational equivalence class.We prove that LGES recovers the true equivalence class in the sample limit from observational and interventional data, even with misspecified prior assumptions.Experiments demonstrate that LGES outperforms GES and other baselines in speed, accuracy, and robustness to misspecified assumptions.