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Invariant Ancestry Search
Phillip Bredahl Mogensen · Nikolaj Thams · Jonas Peters

Thu Jul 21 07:35 AM -- 07:40 AM (PDT) @ Room 309
in Miscellaneous Aspects of Machine Learning »

Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable. If the environments influence only few of the underlying mechanisms, the subset identified by invariant causal prediction (ICP), for example, may be small, or even empty. We introduce the concept of minimal invariance and propose invariant ancestry search (IAS). In its population version, IAS outputs a set which contains only ancestors of the response and is a superset of the output of ICP. When applied to data, corresponding guarantees hold asymptotically if the underlying test for invariance has asymptotic level and power. We develop scalable algorithms and perform experiments on simulated and real data.

Keywords: OPT: Discrete and Combinatorial Optimization · PM: Graphical Models · PM: Structure Learning · MISC: Causality · MISC: Transfer, Multitask and Meta-learning

Author Information

Phillip Bredahl Mogensen (University of Copenhagen)
Nikolaj Thams (University of Copenhagen)
Jonas Peters (University of Copenhagen)

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