Causal Discovery With Continuous Additive Noise Models

Abstract

Causal inference between two observed variables has received a widespread attention in science. Generally, most existing approaches are focusing on inferring the casual direction based on data of the same type. However, in practice, it is very common that the observations obtained from different measurements can have different data types. This issue has not been much explored by the causal inference community. In this paper, we generalize the Additive Noise Model (ANM) to mixed-type data where one variable is discrete and the other is continuous, and take an information theoretic approach to find an unequal relationship between the forward and the backward. To conduct model estimation, we propose Discrete Regression model and Continuous Classification model to learn the residual entropy. In addition to the theoretical results, empirical results on synthetic and real data have also demonstrated the effectiveness of our proposed model.

Keywords

• Causal inference
• Classification
• Mixed type data

This work was partially supported by the National Key Research and Development Program of China (No. 2018AAA0100204).

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Authors and Affiliations

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

   Xin Liu

2. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, China

   Zenglin Xu

3. School of System Science, Beijing Normal University, Haidian, Beijing, China

   Ping Guo

Corresponding author

Correspondence to Zenglin Xu .

Editor information

Editors and Affiliations

1. Department of AI, Ping An Life, Shenzhen, China

   Haiqin Yang

2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

   Dr. Kitsuchart Pasupa

3. City University of Hong Kong, Kowloon, China

   Andrew Chi-Sing Leung

4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

   Prof. James T. Kwok

5. School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

   Jonathan H. Chan

6. The Chinese University of Hong Kong, New Territories, Hong Kong

   Prof. Irwin King

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Liu, X., Xu, Z., Guo, P. (2020). Causal Inference for Mixed-Type Data in Additive Noise Models. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_19

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• DOI : https://doi.org/10.1007/978-3-030-63833-7_19

• Published: 20 November 2020

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• Print ISBN: 978-3-030-63832-0

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