CAUSAL INFERENCE FOR AI

Catalog description

Explores causal inference for Artificial Intelligence and is situated at the intersection of probability, AI, and statistics. Focuses on answering causal and counterfactual queries using causal Bayesian networks and applying causal inference techniques to problems in AI and Machine Learning. Includes causal effect identification, causal discovery algorithms, and addressing challenges such as missing data, selection bias, and interference.