# Amortized Inference for Causal Structure Learning

@article{Lorch2022AmortizedIF, title={Amortized Inference for Causal Structure Learning}, author={Lars Lorch and Scott Sussex and Jonas Rothfuss and Andreas Krause and Bernhard Scholkopf}, journal={ArXiv}, year={2022}, volume={abs/2205.12934} }

Learning causal structure poses a combinatorial search problem that typically involves evaluating structures using 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 the process of causal structure learning . Rather than searching over causal structures directly, we train a variational inference model to predict the causal structure from observational/interventional…

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