Learning to Induce Causal Structure

About

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.

Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Anirudh Goyal, Jorg Bornschein, Melanie Rey, Theophane Weber, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende · 2022

Related benchmarks

Task	Dataset	Result
Causal Discovery	FoundCause 10% MAR missingness	AUROC0.717	16
Causal Discovery	Causal Discovery Suite 30% MAR missingness	Coverage15	16
Causal Discovery	15 Real-world Causal Discovery Benchmark Datasets	Coverage15	16
Bivariate Causal Discovery	Tübingen Cause-Effect Pairs	Correct / Total57	12
Causal Discovery	TEP	SHD49.77	8
Causal Discovery	Food	SHD105.3	8