# Quantifying directed dependence via dimension reduction

arXiv: https://arxiv.org/abs/2112.10147 · HTML: https://mlsanity.com/paper/2112.10147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Kernel Integrated R2: A Measure of Dependence](https://mlsanity.com/paper/2602.22985/) (2026)

## Abstract (arXiv)

Studying the multivariate extension of copula correlation yields a dimension reduction principle, which turns out to be strongly related with the `simple measure of conditional dependence' T recently introduced by Azadkia & Chatterjee (2021). In the present paper, we identify and investigate the dependence structure underlying this dimension-reduction principle, provide a strongly consistent estimator for it, and demonstrate its broad applicability. For that purpose, we define a bivariate copula capturing the scale-invariant extent of dependence of an endogenous random variable Y on a set of d ≥ 1 exogenous random variables bf X = (X1, dots, Xd), and containing the information whether Y is completely dependent on bf X, and whether Y and bf X are independent. The dimension reduction principle becomes apparent insofar as the introduced bivariate copula can be viewed as the distribution function of two random variables Y and Y^prime sharing the same conditional distribution and being conditionally independent given bf X. Evaluating this copula uniformly along the diagonal, i.e. calculating Spearman's footrule, leads to Azadkia and Chatterjee's `simple measure of conditional dependence' T. On the other hand, evaluating this copula uniformly over the unit square, i.e. calculating Spearman's rho, leads to a distribution-free coefficient of determination (a.k.a. copula correlation). Several real data examples illustrate the importance of the introduced methodology.