# Test and Measure for Partial Mean Dependence Based on Machine Learning Methods

arXiv: https://arxiv.org/abs/2212.12874 · HTML: https://mlsanity.com/paper/2212.12874/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches](https://mlsanity.com/paper/2609.00946/) (2026)
- [SPARK: A General Goodness-of-Fit Assessment via Residual Projection](https://mlsanity.com/paper/2609.37705/) (2026)

## Abstract (arXiv)

It is of importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a significance test for the partial mean independence problem based on machine learning methods and data splitting. The test statistic converges to the standard chi-squared distribution under the null hypothesis while it converges to a normal distribution under the fixed alternative hypothesis. Power enhancement and algorithm stability are also discussed. If the null hypothesis is rejected, we propose a partial Generalized Measure of Correlation (pGMC) to measure the partial mean dependence of Y given W after controlling for the nonlinear effect of Z. We present the appealing theoretical properties of the pGMC and establish the asymptotic normality of its estimator with the optimal root-N convergence rate. Furthermore, the valid confidence interval for the pGMC is also derived. As an important special case when there are no conditional covariates Z, we introduce a new test of overall significance of covariates for the response in a model-free setting. Numerical studies and real data analysis are also conducted to compare with existing approaches and to demonstrate the validity and flexibility of our proposed procedures.