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A causal direction test for heterogeneous populations

Vahid Partovi Nia, LI Xin-lin, Masoud Asgharian and Shoubo Hu

Article (2022)

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A probabilistic expert system emulates the decision-making ability of a human expert through a directional graphical model. The first step in building such systems is to understand data generation mechanism. To this end, one may try to decompose a multivariate distribution into product of several conditionals, and evolving a blackbox machine learning predictive models towards transparent cause-and-effect discovery. Most causal models assume a single homogeneous population, an assumption that may fail to hold in many applications. We show that when the homogeneity assumption is violated, causal models developed based on such assumption can fail to identify the correct causal direction. We propose an adjustment to a commonly used causal direction test statistic by using a $k$-means type clustering algorithm where both the labels and the number of components are estimated from the collected data to adjust the test statistic. Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data. We study large sample behaviour of our proposed test statistic and demonstrate the application of the proposed method using real data.

Uncontrolled Keywords

Bayesian hierarchical model; Causal inference; Clustering; Graphical models; Belief network; Probabilistic expert systems; Testing statistical hypotheses

Subjects: 2950 Applied mathematics > 2960 Mathematical modelling
3000 Statistics and probability > 3008 Applied probability
Department: Department of Mathematics and Industrial Engineering
PolyPublie URL: https://publications.polymtl.ca/54332/
Journal Title: Machine learning with applications (vol. 7)
Publisher: Elsevier BV
DOI: 10.1016/j.mlwa.2021.100235
Official URL: https://doi.org/10.1016/j.mlwa.2021.100235
Date Deposited: 02 Nov 2023 13:53
Last Modified: 09 Apr 2024 09:09
Cite in APA 7: Partovi Nia, V., Xin-lin, L.I., Asgharian, M., & Hu, S. (2022). A causal direction test for heterogeneous populations. Machine learning with applications, 7, 100235 (8 pages). https://doi.org/10.1016/j.mlwa.2021.100235


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