# Causal Inference on Discrete Data via Estimating Distance Correlations

arXiv: https://arxiv.org/abs/1803.07712 · HTML: https://mlsanity.com/paper/1803.07712/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [Ordinal Causal Discovery](https://mlsanity.com/paper/2201.07396/) (2022)
- [Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation](https://mlsanity.com/paper/2209.08579/) (2022)
- [Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model](https://mlsanity.com/paper/2303.08572/) (2023)
- [Ordering-based Causal Discovery via Generalized Score Matching](https://mlsanity.com/paper/2601.16249/) (2026)
- [Sample Observed Effects: Enumeration, Randomization and Generalization](https://mlsanity.com/paper/2108.04376/) (2021)
- [A Subsampling-Based Method for Causal Discovery on Discrete Data](https://mlsanity.com/paper/2108.13984/) (2021)
- [Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables](https://mlsanity.com/paper/2403.06499/) (2024)
- [Differentiable Structure Learning and Causal Discovery for General Binary Data](https://mlsanity.com/paper/2509.21658/) (2025)

## Abstract (arXiv)

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.