Bayesian Causal Inference with Gaussian Process Networks

1 code implementation • 1 Feb 2024 • Enrico Giudice, Jack Kuipers, Giusi Moffa

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.

Causal Discovery Causal Inference +1

A Bayesian Take on Gaussian Process Networks

1 code implementation • NeurIPS 2023 • Enrico Giudice, Jack Kuipers, Giusi Moffa

Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network.

Gaussian Processes

The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks

1 code implementation • 16 Dec 2021 • Enrico Giudice, Jack Kuipers, Giusi Moffa

Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges.