Causality: Counterfactuals | Part B

Tutorial on causal inference, covering the basics of counterfactual thinking. Topics: causal mechanisms and why we need them for counterfactual reasoning; types of models needed for counterfactual reasoning: functional Bayesian networks and structural causal models; probabilities of causation; twin-network technique; why interventional reasoning is not as refined as counterfactual reasoning; syntax and semantics of counterfactual queries; open-ended counterfactual queries; and identifiability of counterfactual queries (point estimates and bounds) based on observational and experimental data. Includes discussion of prototypical counterfactual queries such as probability of necessity (PN), probability of sufficiency (PS) and probability of necessity and sufficiency (PNS). 00:00 Agenda 00:58 The Information Hierarchy 02:09 Structural Causal Models 06:32 Syntax and Semantics of Counterfactual Queries 09:56 Probabilities of Counterfactuals 12:54 Events: observational, interventional, counterfactual 19:56 Example: Computing Probabilities of Counterfactuals 28:18 Beyond Prototypical Counterfactual Quantities 30:25 The Importance of Notation 42:11 Concluding Remarks --- Slides available at: http://web.cs.ucla.edu/~darwiche/caus... --- On Pearl's Causal Hierarchy and the Foundations of Causal Inference: https://causalai.net/r60.pdf --- Causal Inference Using Tractable Circuits: https://arxiv.org/pdf/2202.02891.pdf