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