Learning from Observational Data

gsp(nodes, ci_tester[, depth, nruns, ...])	Estimate the Markov equivalence class of a DAG using the Greedy Sparsest Permutations (GSP) algorithm.
pcalg(nodes[, ci_tester, skel, sepset, ...])	Use the PC (Peters-Clark) algorithm to estimate the Markov equivalence class of the data-generating DAG.

Learning from Interventional Data

igsp(setting_list, nodes, ci_tester, ...[, ...])	TODO
unknown_target_igsp(setting_list, nodes, ...)	Use the Unknown Target Interventional Greedy Sparsest Permutation algorithm to estimate a DAG in the I-MEC of the data-generating DAG.

Bayesian Methods

Active Learning