Enrico Giudice: Bayesian Causal Inference with Gaussian Process Networks
23 November 2023
East Campus USI-SUPSI, Room C2.09
Causal inference from observational data is a compelling problem in statistics, which has attracted much attention due to its potential application in various scientific fields. Estimating the effects of a manipulation on a system of random variables however poses both modeling and computational challenges, which are typically addressed by imposing strict assumptions on the joint distribution such as linearity. One appealing approach is to model the system as a Gaussian process network (GPN), which allows describing the causal relationships among a set of random variables with minimal parametric assumptions. In the absence of prior knowledge of the underlying causal graph, a fully Bayesian approach requires integrating the causal quantity of interest over the posterior over graphs, which is computationally infeasible even in low dimensions. By harnessing Monte Carlo and Markov Chain Monte Carlo methods we can sample from the posterior distribution of network structures, thus providing an accurate approximation of the posterior. Causal inference across the whole GPN can then be performed while also accounting for uncertainty in the causal graph. Simulation studies show that our approach is able to identify the effects of hypothetical interventions with non-Gaussian, non-linear observational data and accurately reflect the posterior uncertainty of the causal estimates. Finally we compare the results of our GPN-based causal inference approach to existing methods on a real dataset of A. Thaliana gene expressions.

The Speaker

Enrico Giudice is a PhD Candidate at the Department of Mathematics and Computer Science University of Basel. The work on Gaussian Process Networks presented during the talk has been accepted at Neurips 2023 (https://arxiv.org/abs/2306.11380).

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