Philosophy

This talk will give a broad coverage of central concepts and principles involved in automated causal inference and emerging approaches to causal discovery. After reviewing concepts including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models, I will present the constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, and discuss the assumptions underlying its validity. I will then focus on causal discovery based on structural equations models, in which a key issue is the identifiability of the causal structure implied by appropriately defined structural equation models. Finally, I list a number of open questions in the field of causal discovery and inference.