An Introduction to Causal Inference

An Introduction to Causal Inference

This summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.
Country USA
Author Judea Pearl
Binding Kindle Edition
Format Kindle eBook
IsAdultProduct
NumberOfPages 108
PublicationDate 2014-04-29
ReleaseDate 2014-04-29

Country	USA
Author	Judea Pearl
Binding	Kindle Edition
Format	Kindle eBook
IsAdultProduct
NumberOfPages	108
PublicationDate	2014-04-29
ReleaseDate	2014-04-29

An Introduction to Causal Inference

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