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On the Generalization and Adaption Performance of Causal Models
Nino Scherrer · Anirudh Goyal · Stefan Bauer · Yoshua Bengio · Rosemary Nan Ke
Event URL: https://openreview.net/forum?id=xPehcJaCol- »

Learning models that offer robust out-of-distribution generalization and fast adaptation is a key challenge in modern machine learning. Modelling causal structure into neural networks holds the promise to accomplish robust zero and few-shot adaptation. Recent advances in differentiable causal discovery have proposed to factorize the data generating process into a set of modules, i.e. one module for the conditional distribution of every variable where only causal parents are used as predictors. Such a modular decomposition of knowledge allows to adapt to distributions shifts by only updating a subset of parameters. In this work, we systematically study the generalization and adaption performance of such causal models by comparing it to monolithic models and structured models where the set of predictors is not constrained to causal parents. Our analysis shows that causal models outperform other models on both zero and few-shot adaptation in low data regimes and offer robust generalization. We also found that the effects are more significant for sparser graphs as compared to denser graphs.

Author Information

Nino Scherrer (ETH Zürich / Mila)
Anirudh Goyal (Université de Montréal)
Stefan Bauer (KTH Stockholm)
Yoshua Bengio (Mila - Quebec AI Institute)
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/

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