Poster

Invariant Ancestry Search

Phillip Bredahl Mogensen · Nikolaj Thams · Jonas Peters

Hall E #532

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


Abstract:

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.

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