Generating Counterfactual Explanations For Causal Inference in Breast Cancer Treatment Response.

Imaging phenotypes extracted via radiomics of magnetic resonance imaging has shown great potential at predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Existing machine learning models are, however, limited in providing an expert-level interpretation of these models, particularly interpretability towards generating causal inference. Causal relationships between imaging phenotypes, clinical information, molecular features, and the treatment response may be useful in guiding the treatment strategies, management plans, and gaining acceptance in medical communities. In this work, we leverage the concept of counterfactual explanations to extract causal relationships between various imaging phenotypes, clinical information, molecular features, and the treatment response after NST. We implement the methodology on a publicly available breast cancer dataset and demonstrate the causal relationships generated from counterfactual explanations. We also compare and contrast our results with traditional explanations, such as LIME and Shapley.