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Poster
Amortized Inference for Causal Structure Learning
Lars Lorch · Scott Sussex · Jonas Rothfuss · Andreas Krause · Bernhard Schölkopf

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #105
in Poster Session 5 »

Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is difficult. In this work, we propose to amortize causal structure learning. Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data. This allows our inference model to acquire domain-specific inductive biases for causal discovery solely from data generated by a simulator, bypassing both the hand-engineering of suitable score functions and the search over graphs. The architecture of our inference model emulates permutation invariances that are crucial for statistical efficiency in structure learning, which facilitates generalization to significantly larger problem instances than seen during training. On synthetic data and semisynthetic gene expression data, our models exhibit robust generalization capabilities when subject to substantial distribution shifts and significantly outperform existing algorithms, especially in the challenging genomics domain. Our code and models are publicly available at: https://github.com/larslorch/avici

Keywords: Causality · Structure Learning · causal discovery · Bayesian causal discovery · amortized inference
[ Paper]  [ Poster]  [ OpenReview]  [ Topia] 

Author Information

Lars Lorch (ETH Zürich)
Scott Sussex (Swiss Federal Institute of Technology)
Jonas Rothfuss (ETH Zurich)
Andreas Krause (ETH Zurich)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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