Identifying intervention strategies from machine learning models with COALA: a counterfactual optimization framework

Abstract

MotivationMachine learning (ML) models have become increasingly complex, often functioning as black boxes that limit our understanding of the features contributing to ML predictions. Common explainable AI (XAI) methods such as SHapley Additive exPlanations (SHAP) focus on feature importance but fall short in identifying interactions among features and informing targeted, personalized interventions. Counterfactuals are hypothetical events where specific variables are altered to cause a change in outcome. These causal statements can be applied to AI models to identify actionable interventions for different subjects in a population. ResultsWe propose the framework Counterfactual Optimization for Actionable interpretabiLity in AI (COALA). COALA interprets models by identifying optimal counterfactuals for each subject, which are defined as actionable changes that lead to the most positive change in predicted outcome. When applied to a gradient boosted tree model trained on the National Health and Nutrition Examination Survey (NHANES) dataset, COALA identifies different profiles of optimal counterfactuals across subjects. Features that remain constrained were able to predict the optimal counterfactual changes for a subject at 85.4%, revealing specific features that drive what a subjects optimal counterfactual is. Availability and ImplementationCode for COALA implementation, synthetic data, models trained on synthetic data, and code to replicate results and figures are available at https://github.com/brt-solo/COALA. The NHANES 2017-2018 dataset is publicly available from the National Center for Health Statistics (NCHS). The Framingham Heart Study dataset used in this study is a publicly available, Framingham-derived dataset distributed through the Massachusetts Institute of Technology OpenCourseWare (MIT OCW) repository.