Inferring Causal Directions in Errors-in-Variables Models

Yulai Zhang; Guiming Luo

doi:10.1609/aaai.v28i1.9079

Inferring Causal Directions in Errors-in-Variables Models

Authors

Yulai Zhang Tsinghua University
Guiming Luo Tsinghua University

DOI:

https://doi.org/10.1609/aaai.v28i1.9079

Abstract

Inferring the causal direction between two variables is a nontrivial problem in the subject of causal discovery from observed data. A method for errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is presented in this paper.

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Published

2014-06-21

How to Cite

Zhang, Y., & Luo, G. (2014). Inferring Causal Directions in Errors-in-Variables Models. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.9079