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

Zhalama, Mr. and Zhang, Jiji and Eberhardt, Frederick and Mayer, Wolfgang and Li, Mark Junjie (2019) ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200527-101434154

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1906.02385arXivDiscussion Paper
Additional Information:JZ was supported by GRF LU13602818 from the RGC of Hong Kong. FE was supported by NSF grant 1564330.
Funders:
Funding AgencyGrant Number
Research Grants Council of Hong KongGRF LU13602818
NSFIIS-1564330
Record Number:CaltechAUTHORS:20200527-101434154
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200527-101434154
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103489
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 May 2020 17:20
Last Modified:27 May 2020 17:20

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