Counterfactual Theory

Counterfactual theory explores causal relationships by considering what would have happened under different circumstances, aiming to understand cause-and-effect beyond simple correlations. Current research focuses on applying this framework to complex systems like neural networks and decision-support systems, often employing structural causal models and potential outcomes frameworks to address challenges like identifying multiple sufficient causes and mitigating unintended consequences. This work is significant for improving the interpretability and trustworthiness of AI models, as well as for designing more ethical and effective decision-making systems across various domains.