Causality Models, Reasoning and Inference: Causal Data Science Course

DSA ADS Course - 2021

Causal Analysis, Causality Models, Causal Inference

Discuss causal analysis and how it applies to many different fields like health policy and medical practice, public policy, business decisions, statistics, artificial intelligence, economics, philosophy, cognitive science, and others. Review probabilistic, manipulative, counterfactual, and structural approaches to causation and apply simple mathematical tools for studying the relationships between causal connections and statistical associations.

Discuss inferential procedures and precise mathematical definitions of causal concepts and distinguish from traditional methods of statistical thinking. Apply to find meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, and form theories of causal understanding and causal speech.

2013 - Causality Models, Reasoning and Inference - Chapter 11 by Judea Pearl

See attached. See also textbook at Causality: Models, Reasoning, and Inference

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