Causal Discovery In the Presence of Selection Bias

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

In this work, 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. 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. We also propose two methods for estimating an additive noise model from data that are generated with outcome-dependent selection. [paper]