DIET: Conditional independence testing with marginal dependence measures of residual information.
Year: 2023
Type: article
Abstract: . These variables are termed "information residuals." We give sufficient conditions for DIET to achieve finite sample type-1 error control and power greater than the type-1 error rate. We then prove that when using the mutual information between the information residuals as a test statistic, DIET yields the most powerful conditionally valid test. Finally, we show DIET achieves higher power than other tractable CRTs on several synthetic and real benchmarks.
Source: PubMed
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Deep probabilistic predictive models for stroke and coronary heart disease, NRT-HDR: FUTURE Foundations, Translation, and Responsibility for Data Science Impact, G6671297155, Career: Building Models that Avoid Spurious Correlations through Interpretability and Representation Learning, Cancer Center Support Grant, Probabilistic Multiscale Modeling of the Tumor Microenvironment, The Center for Tumor-Immune Systems Biology at MSKCC
OpenAlex ID: W4386529230