Counterfactual Distribution

Counterfactual distribution research focuses on estimating what would have happened under different conditions, addressing fundamental questions of causality and enabling "what-if" analysis. Current research emphasizes developing methods to accurately estimate these distributions, particularly in complex settings with high-dimensional data, unobserved confounders, and dynamic systems, employing techniques like generative adversarial networks, optimal transport, and Bayesian approaches within various model architectures including neural networks and Gaussian processes. This work has significant implications for diverse fields, improving the interpretability of machine learning models, facilitating root cause analysis in dynamic systems, and enabling fairer and more robust decision-making in areas such as healthcare and social sciences.