On the identifiability and estimation of functional causal models in the presence of outcome-dependent selection

Kun ZHANG, Jiji ZHANG, Biwei HUANG, Bernhard SCHÖLKOPF, Clark GLYMOUR

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

5 Citations (Scopus)

Abstract

We study the identifiability and estimation of functional causal models under selection bias, with a focus on the situation where the selection depends solely on the effect variable, which is known as outcome-dependent selection. We address two questions of identifiability: the identifiability of the causal direction between two variables in the presence of selection bias, and, given the causal direction, the identifiability of the model with outcome-dependent selection. Regarding the first, we show that in the framework of post-nonlinear causal models, once outcome-dependent selection is properly modeled, the causal direction between two variables is generically identifiable; regarding the second, we identify some mild conditions under which an additive noise causal model with outcome-dependent selection is to a large extent identifiable. We also propose two methods for estimating an additive noise model from data that are generated with outcome-dependent selection.
Original languageEnglish
Title of host publicationProceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
PublisherAUAI Press
Pages825-834
Number of pages10
ISBN (Print)9781510827806
Publication statusPublished - 1 Jan 2016

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