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Learning to induce causal structure
Rosemary Nan Ke · Silvia Chiappa · Jane Wang · Jorg Bornschein · Anirudh Goyal · Melanie Rey · Matthew Botvinick · Theophane Weber · Michael Mozer · Danilo J. Rezende
Event URL: https://openreview.net/forum?id=dhGFrNx85nd »

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 \emph{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.

Author Information

Rosemary Nan Ke (MILA, University of Montreal)

I am a PhD student at Mila, I am advised by Chris Pal and Yoshua Bengio. My research interest are efficient credit assignment, causal learning and model-based reinforcement learning. Here is my homepage https://nke001.github.io/

Silvia Chiappa (DeepMind)
Jane Wang (DeepMind)
Jorg Bornschein (DeepMind)
Anirudh Goyal (Université de Montréal)
Melanie Rey (BenevolentAI)
Matthew Botvinick (Princeton University)
Theophane Weber (DeepMind)
Michael Mozer (Google Research)
Danilo J. Rezende (DeepMind)
Danilo J. Rezende

Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.

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