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Interaction Analysis Based on Shapley Values and Extreme Gradient Boosting: A Realistic Simulation and Application to
Nicola Orsini1, Alex Moore2, Alicja Wolk3,4
1Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
Frontiers in Nutrition
|August 4, 2022
Summary
SHapley Additive exPlanations (SHAP) effectively detect interaction effects in observational studies, with accuracy increasing with effect magnitude. SHAP values also reliably identify the direction of these interactions across various scenarios.
Area of Science:
- Epidemiology
- Biostatistics
- Machine Learning
Background:
- SHapley Additive exPlanations (SHAP) are used for interpreting machine learning models in observational studies.
- The performance of SHAP in realistic simulations for exposure interactions is not well-established.
Purpose of the Study:
- To evaluate the performance of SHAP in detecting and characterizing interaction effects in realistic simulation scenarios.
- To compare SHAP-based estimates with traditional logistic regression for interaction analysis.
Main Methods:
- A realistic simulation was created using data from 47,770 individuals from Swedish population-based cohorts.
- Three scenarios with small, moderate, and large discrepancies in mortality odds ratios between men and women were simulated.
- Estimates were derived using logistic regression and SHAP values.
Main Results:
- SHAP demonstrated high sensitivity in detecting interaction effects, with sensitivity increasing from 28% (small) to 100% (large) discrepancies.
- The sensitivity for correctly identifying the sign (direction) of the interaction effect was high for both logistic regression (93-100%) and SHAP (86-100%).
Conclusions:
- SHAP's ability to detect interaction effects is proportional to their magnitude in realistic simulations.
- SHAP values provide a highly sensitive and reliable method for identifying the direction of interaction effects in observational studies.
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