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.
|Title of host publication||Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)|
|Number of pages||10|
|Publication status||Published - 1 Jan 2016|
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 Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016) (pp. 825-834). AUAI Press.