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B0423
Title: Bayesian inference of causal effects from Gaussian graphical models Authors:  Federico Castelletti - Università Cattolica del Sacro Cuore (Milan) (Italy) [presenting]
Abstract: It is assumed that multivariate observational data are generated from a distribution whose conditional independencies are encoded in a Directed Acyclic Graph (DAG). For any given DAG, the causal effect of a variable onto another one can be evaluated through intervention calculus. A DAG is typically not identifiable from observational data alone. However, its Markov equivalence class (a collection of DAGs) can be estimated from the data. As a consequence, for the same intervention, a set of causal effects, one for each DAG in the equivalence class, can be evaluated. We propose a Bayesian methodology which combines structure learning of DAGs and causal effect estimation. As a consequence, our approach fully accounts for the uncertainty around the underlying graphical structure, which is crucial for a correct estimation of the causal effect of an intervention on one variable w.r.t. another. We demonstrate the merits of our method in simulation studies, wherein comparisons with current state-of-the-art procedures turn out to be highly satisfactory. Finally, we examine a real data set of gene expressions for Arabidopsis thaliana.