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Sample Efficient Active Learning of Causal Trees

We consider the problem of experimental design for learning causal graphs that have a tree structure. We propose an adaptive framework …

Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

We show that can use labelled data in source domains and unlabelled data in the target domain to identify features that are robust to the specific shift that happens in the target dataset. While we use ideas from causality, we do not need to recover the causal graph (which in this case is not identifiable) to find these sets of features.

Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M Mooij