Learning to Induce Causal Structure

• URL: http://arxiv.org/abs/2204.04875v1
• Date: Mon, 11 Apr 2022 05:38:22 GMT
• Title: Learning to Induce Causal Structure
• Authors: Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jorg Bornschein, Theophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende
• Abstract summary: We propose a neural network architecture that learns the mapping from both observational and interventional data to graph structures via supervised training on synthetic graphs. We show that the proposed model generalizes not only to new synthetic graphs but also to naturalistic graphs.
• Score: 29.810917060087117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms operate by generating candidate graphs and then evaluating them using either score-based methods (including continuous optimization) or independence tests. In this work, instead of proposing scoring function or independence tests, we treat the inference process as a black box and design a neural network architecture that learns the mapping from both observational and interventional data to graph structures via supervised training on synthetic graphs. We show that the proposed model generalizes not only to new synthetic graphs but also to naturalistic graphs.

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