Causal Inference
Causal inference aims to determine cause-and-effect relationships from data, going beyond mere correlations to understand how interventions impact outcomes. Current research heavily focuses on addressing challenges like confounding (the influence of unobserved variables), particularly in high-dimensional data and complex treatments (e.g., text, sequences of actions), employing methods such as structural causal models, Bayesian Additive Regression Trees (BART), and various neural network architectures including Graph Neural Networks (GNNs). These advancements are crucial for improving the reliability of causal conclusions across diverse fields, from medicine and economics to personalized interventions and policy-making.
Papers
June 15, 2023
June 9, 2023
June 8, 2023
June 7, 2023
June 6, 2023
May 28, 2023
May 27, 2023
May 26, 2023
May 25, 2023
May 24, 2023
May 23, 2023
May 18, 2023
May 17, 2023
May 11, 2023
May 9, 2023
May 5, 2023