Probability Seminar ''Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score''

December 11, from 3:30 p.m. to 4:30 p.m. (Rio de Janeiro local time)

Local: room C116 - Bloco C - CT – Instituto de Matemática – UFRJ

Speaker:  Widemberg da Silva Nobre (IM-UFRJ)

Abstract: We study Bayesian approaches to causal inference via propensity score regression. Much of Bayesian methodology relies on parametric and distributional assumptions, with presumed correct specification, whereas the extant propensity score methods in Bayesian literature have relied on approaches that cannot be viewed as fully Bayesian in the context of conventional ‘likelihood times prior’ posterior inference. We emphasize that causal inference is typically carried out in settings of mis-specification, and develop strategies for fully Bayesian inference that reflect this. We focus on methods based on decision-theoretic arguments, and show how inference based on loss-minimization can give valid and fully Bayesian inference. We propose a computational approach to inference based on the Bayesian bootstrap which has good Bayesian and frequentist properties.

Link of the paper HERE.

More complete information about the seminars can be found at DME.

Sincerely,

Organizers: Giulio Iacobelli e Maria Eulalia Vares