Causal Inference on Discrete Data via Estimating Distance Correlations
This addresses causal inference for discrete data, which is an incremental improvement in a specific domain.
The paper tackles the problem of inferring causal directions from discrete data by comparing distance correlations between distributions, proposing to infer 'X causes Y' if the dependence between P(X) and P(Y|X) is smaller, with experiments demonstrating its performance.
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.