Causal inference from observational data: A Bayesian predictive approach

• Published in: Causality Vol. 986; pp. 71 - 84
• Main Author: Arjas, Elja
• Format: Book Chapter
• Language: English
• Published: United Kingdom Wiley 2012; John Wiley & Sons, Ltd
• Subjects: 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
• ISBN: 0470665564; 9780470665565
• DOI: 10.1002/9781119945710.ch7

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