Causal inference from observational data: A Bayesian predictive approach

This chapter contains sections titled: Background A model prototype Extension to sequential regimes Providing a causal interpretation: Predictive inference from data Discussion Acknowledgement References

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Published inCausality Vol. 986; pp. 71 - 84
Main Author Arjas, Elja
Format Book Chapter
LanguageEnglish
Published United Kingdom Wiley 2012
John Wiley & Sons, Ltd
Subjects
Online AccessGet full text
ISBN0470665564
9780470665565
DOI10.1002/9781119945710.ch7

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Abstract This chapter contains sections titled: Background A model prototype Extension to sequential regimes Providing a causal interpretation: Predictive inference from data Discussion Acknowledgement References
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Dawid, Philip
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Snippet This chapter contains sections titled: Background A model prototype Extension to sequential regimes Providing a causal interpretation: Predictive inference...
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StartPage 71
SubjectTerms causal inference from observational data, Bayesian predictive
causality, and temporality, confounding, statistical inference
effects of causes, in terms of posterior predictive distributions/expectations
G‐computation algorithm, on model parameter and treatment assignments
marked point formalism, and latent variables and developments
marked point sequence and counting processes, in continuous time
MATHEMATICS
potential, or counterfactual outcomes, drawback of sequential regimes
Robins' model, extension of Rubin causal model to sequential designs
unconfounded inference conditions, against bias in causal conclusions
unconfounded statistical inferences, from observational data
Title Causal inference from observational data: A Bayesian predictive approach
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