Causal Inference on Discrete Data via Estimating Distance Correlations

Furui Liu; Laiwan Chan

doi:10.1162/neco_a_00820

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arXiv · 1803.07712

Causal Inference on Discrete Data via Estimating Distance Correlations

Furui Liu·Laiwan Chan

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Abstract

In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause $P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as independent random variables, we propose to infer the causal direction via comparing the distance correlation between $P(X)$ and $P(Y|X)$ with the distance correlation between $P(Y)$ and $P(X|Y)$. We infer "$X$ causes $Y$" if the dependence coefficient between $P(X)$ and $P(Y|X)$ is smaller. Experiments are performed to show the performance of the proposed method.

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BibTeX RIS

Furui Liu, Laiwan Chan. 2018-03-21. Causal Inference on Discrete Data via Estimating Distance Correlations. https://doi.org/10.1162/neco_a_00820

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