Title

Adjacency-faithfulness and conservative causal inference

Document Type

Book chapter

Source Publication

Proceedings of the Twenty-Second Conference Conference on Uncertainty in Artificial Intelligence (2006)

Publication Date

1-1-2006

First Page

401

Last Page

408

Publisher

AUAI Press

Abstract

Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) 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 in principle possible to test the validity of Orientation-Faithfulness. Based on 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 (asymptotically) correct under the weaker, Adjacency-Faithfulness assumption. Roughly 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. However, if the stronger, standard causal Faithfulness condition actually obtains, the CPC algorithm is shown to output the same pattern as the PC algorithm does in the large sample limit. We also present a simulation study showing that the CPC algorithm runs almost as fast as the PC algorithm, and outputs significantly fewer false causal arrowheads than the PC algorithm does on realistic sample sizes. We end our paper by discussing how score-based algorithms such as GES perform when the Adjacency-Faithfulness but not the standard causal Faithfulness condition holds, and how to extend our work to the FCI algorithm, which allows for the possibility of latent variables.

Publisher Statement

Copyright © UAI 2006, AUAI Press.

Access to external full text or publisher's version may require subscription.

Additional Information

ISBN of the source publication: 0974903922

Full-text Version

Publisher’s Version

Recommended Citation

Ramsey, J., Zhang, J., & Spirtes, P. (2006). 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) (pp.401-408). Arlington, Virginia: AUAI Press. Retrieved from https://dslpitt.org/uai/papers/06/p401-ramsey.pdf