Counterfactual Explanation Method

Counterfactual explanation methods aim to enhance the transparency of machine learning models by identifying minimal input changes that alter a model's prediction. Current research focuses on improving the efficiency and robustness of these methods, particularly through the use of normalizing flows and other generative models, addressing challenges like computational cost and handling categorical data. This work is significant for increasing trust and understanding of complex models across diverse applications, from medical image analysis and employee attrition prediction to more general tabular data analysis, by providing actionable insights into model decisions.