ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions

Abstract

In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may help to speed up methods based on Answer Set Programming. However, this line of work has so far only considered the discovery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.

Comparing Causal Bayesian Networks Estimated from Data

“…The faithfulness assumption establishes a one-to-one correspondence between the structure of the data generation process (i.e., the causal structure) and the statistical properties of the data, thus making causal discovery from observational data possible. Several more recent methods have sought to discover causal structure under weaker versions of faithfulness or specific types of faithfulness violation [21][22][23][24].…”