Counterfactual Estimation

Counterfactual estimation aims to determine the likely outcome of an event had a different action been taken, addressing the fundamental challenge of causal inference from observational data. Current research focuses on improving the accuracy and efficiency of counterfactual estimation, particularly in time-series data and with multiple treatments, employing diverse methods such as generative adversarial networks, graph neural networks, and Bayesian hierarchical models. These advancements have significant implications across various fields, including healthcare (e.g., personalized medicine, treatment optimization), economics (e.g., policy evaluation), and marketing (e.g., campaign effectiveness), by enabling more robust and reliable causal analyses.