Causal Inference on Discrete Data via Estimating Distance Correlations

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.

References in corpus (2)

• Measuring and testing dependence by correlation of distances
• Causal Inference on Discrete Data using Additive Noise Models

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• A Subsampling-Based Method for Causal Discovery on Discrete Data
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