On the identifiability and estimation of functional causal models in the presence of outcome-dependent selection
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
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.
Research reported in this publication was supported by the National Institutes of Health (NIH) under Award Number U54HG008540. The research of J. Zhang was supported in part by the Research Grants Council of Hong Kong under the General Research Fund LU342213.
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ISBN of the source publication: 9781510827806
Zhang, K., Zhang, J., Huang, B., Schölkopf, B., & Glymour, C. (2016). On the identifiability and estimation of functional causal models in the presence of outcome-dependent selection. In A. Ihler & D. Janzing (Eds.), Proceedings of the 32nd conference on uncertainty in artificial intelligence (UAI 2016) (pp. 825--834). Red Hook, NY: AUAI Press.