Learning to Induce Causal Structure

Nan Rosemary Ke; Silvia Chiappa; Jane Wang; Anirudh Goyal; Jorg Bornschein; Melanie Rey; Theophane Weber; Matthew Botvinic; Michael Mozer; Danilo Jimenez Rezende

arXiv · 2204.04875

Learning to Induce Causal Structure

Nan Rosemary Ke·Silvia Chiappa·Jane Wang·Anirudh Goyal·Jorg Bornschein·Melanie Rey·Theophane Weber·Matthew Botvinic·Michael Mozer·Danilo Jimenez Rezende

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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 evaluating them using either score-based methods (including continuous optimization) or independence tests. In our work, we instead 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. The learned model generalizes to new synthetic graphs, is robust to train-test distribution shifts, and achieves state-of-the-art performance on naturalistic graphs for low sample complexity.

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BibTeX RIS

Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Anirudh Goyal, Jorg Bornschein, Melanie Rey, Theophane Weber, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende. 2022-04-11. Learning to Induce Causal Structure. https://arxiv.org/abs/2204.04875

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