Surging interest in deep learning from high-stakes domains has precipitated concern over
the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to …

Designing faithful yet accurate AI models is challenging, particularly in the field of individual
treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as …

The perturbation test remains the go-to evaluation approach for explanation methods in
computer vision. This evaluation method has a major drawback of test-time distribution shift …

Electronic healthcare records are vital for patient safety as they document conditions, plans,
and procedures in both free text and medical codes. Language models have significantly …

As a result of the many recent advancements in artificial intelligence (AI), a significant
interest in the technology has developed from high-stakes decision-makers in industries …

Individual treatment effect is of great importance for healthcare and beyond. While most
existing solutions focus on accurate treatment effect estimations, they rely on non …