Identifying Causal Effects Under Functional Dependencies

Yizuo Chen, Adnan Darwiche

Keywords: Identifiability, Causal Effects, Functional Dependencies

TL;DR: The paper studies the identifiability of causal effects under functional dependencies.

Abstract: We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects.

Primary Area: Causal inference

Submission Number: 13635