Work

On the Role of Priors in Bayesian Causal Learning

Year: 2024

Type: article

Abstract: In this work, we investigate causal learning of independent causal mechanisms (ICMs) from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically accessible manner that unlabeled data (i.e., cause realizations) do not improve the estimation of the parameters defining the mechanism. Furthermore, we observe the importance of choosing an appropriate prior for the cause and mechanism parameters, respectively. Specifically, we show that a factorized prior results in a factorized posterior, which resonates with Janzing and Schölkopf's definition of ICMs via the Kolmogorov complexity of the involved distributions and with the concept of parameter independence of Heckerman et al.

Source: IEEE Transactions on Artificial Intelligence

DOI: 10.1109/tai.2024.3522867

Authors: Bernhard C. Geiger, Roman Kern

Institution: Graz University of Technology

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Austrian Science Fund

Awards: G1486361761, Knowledge-driven fine-tuning of perovskite-based electrode materials for reversible Chemicals-to-Power devices