DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal hypothesis generation using conditional SHAP | JoVE Visualize

Area of Science:

Biomedical Data Science
Causal Inference
Precision Medicine

Background:

Precision medicine requires tailoring healthcare to individual variability in genetic, clinical, and environmental factors.
Standard causal inference methods often overlook population heterogeneity, hindering the identification of subgroup-specific causal relationships.
Complex biomedical data presents challenges in detecting differential causal effects across patient subgroups.

Purpose of the Study:

To introduce DeepDiff-SHAP, a novel framework for detecting changes in causal relationships across patient subgroups.
To integrate deep learning and regression-based methods with conditional SHapley Additive exPlanations (SHAP) for nonlinear differential causal inference.
To provide a scalable and interpretable solution for uncovering individualized causal pathways in precision medicine.

Main Methods:

Developed DeepDiff-SHAP, a framework combining regression-based and deep learning-based differential causal inference.
Integrated conditional SHapley Additive exPlanations (SHAP) to estimate conditional dependencies and perform nonlinear differential causal inference.
Applied the framework to the CDC Diabetes Health Indicators Dataset and a UK Biobank sepsis cohort stratified by hypertension status.

Main Results:

Identified clinically meaningful, subgroup-specific causal changes in feature relationships within population-scale datasets.
Detected differential causal effects related to age, general health, alkaline phosphatase, and cholesterol in the analyzed cohorts.
Demonstrated that deep learning enhances sensitivity to complex interaction patterns missed by linear models.

Conclusions:

DeepDiff-SHAP offers a scalable and interpretable approach to uncover individualized causal pathways, advancing precision medicine.
The framework provides new biological insights into disease progression and comorbidity-specific risk mechanisms.
Differential causal inference using deep learning is crucial for understanding heterogeneity in biomedical data.