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

Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard H Schölkopf, Clark Glymour

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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)
EditorsAlexander Ihler, Dominik Janzing
PublisherAUAI Press
Pages825–834
Number of pages10
ISBN (Print)9780996643115
Publication statusPublished - Jun 2016
Event32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016 - Jersey City, United States
Duration: 25 Jun 201629 Jun 2016
https://www.auai.org/uai2016/ (Conference website)
https://www.auai.org/uai2016/proceedings.php (Conference proceedings)

Conference

Conference32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016
Country/TerritoryUnited States
CityJersey City
Period25/06/1629/06/16
Internet address

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