Adjacency-Faithfulness and Conservative Causal Inference

In R. Dechter & T. Richardson (eds.), Proceedings of the Twenty-Second Conference Conference on Uncertainty in Artificial Intelligence (2006). Arlington, Virginia: AUAI Press. pp. 401-408 (2006)
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Abstract

Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has to be modified to be correct under the weaker, Adjacency-Faithfulness assumption. The modified algorithm, called Conservative PC (CPC), checks whether Orientation- Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. Howtion: ever, if the stronger, standard causal Faith-.

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Author Profiles

Jiji Zhang
Chinese University of Hong Kong
Joseph Ramsey
Carnegie Mellon University

Citations of this work

The Frugal Inference of Causal Relations.Malcolm Forster, Garvesh Raskutti, Reuben Stern & Naftali Weinberger - 2018 - British Journal for the Philosophy of Science 69 (3):821-848.
A comparison of three Occam’s razors for Markovian causal models.Jiji Zhang - 2013 - British Journal for the Philosophy of Science 64 (2):423-448.
Causal Conclusions that Flip Repeatedly and Their Justification.Kevin T. Kelly & Conor Mayo-Wilson - 2010 - Proceedings of the Twenty Sixth Conference on Uncertainty in Artificial Intelligence 26:277-286.

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