Causal Inference on Discrete Data via Estimating Distance Correlations

TL;DR

This paper introduces a method for causal inference on discrete data by comparing distance correlations between distributions, effectively identifying causal directions based on independence assumptions.

Contribution

It proposes a novel approach using distance correlation to infer causality on discrete data by analyzing distribution independence.

Findings

Method accurately infers causal directions in discrete datasets.

Experiments demonstrate the effectiveness of the proposed approach.

Outperforms existing causal inference techniques on tested datasets.

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.