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

arXiv: https://arxiv.org/abs/1906.02385 · HTML: https://mlsanity.com/paper/1906.02385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)](https://mlsanity.com/paper/2309.05264/) (2023)
- [Causal Razors](https://mlsanity.com/paper/2302.10331/) (2023)
- [xWhyL: Causal Interactive Learning](https://mlsanity.com/paper/2609.26037/) (2026)

## Abstract (arXiv)

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.