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Counterfactual formulations of causality can be traced as far
back as Hume. Counterfactual statistical models were first
proposed by Jerzy Neyman in a recently rediscovered paper of
1929. Don Rubin and others have popularized the models
and stressed their importance in the analysis of causal
inferences from observational data. This talk will be a non-technical
survey of the problems of causal inference from observational
data, and the practical implications of the counterfactual
models for inference.
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