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Towards Computing an Optimal Abstraction for Structural Causal Models

Year: 2022

Type: conference-paper

Abstract: Working with causal model at different levels of abstraction is an important feature of science. Existing work has already considered the problem of expressing formally the relation of abstraction between causal models. In this paper, we focus on the problem of learning abstractions. We start by defining the learning problem formally in terms of the optimization of a standard measure of consistency. We then point out the limitation of this approach, and we suggest extending the objective function with a term accounting for information loss. We suggest a concrete measure of information loss, and we illustrate its contribution to learning new abstractions. Accepted for the Causal Representation Learning workshop at the 38th Conference on Uncertainty in Artificial Intelligence (UAI CRL 2022)

Source: Warwick Research Archive Portal (University of Warwick)

DOI: 10.48550/arxiv.2208.00894

Authors: Zennaro, Fabio Massimo, Turrini, Paolo, Damoulas, Theodoros

Institution: University of Warwick

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4289598185

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