Towards Robust Causal Effect Identification beyond Markov Equivalence

Kai Z. Teh, Kayvan Sadeghi, Terry Soo

Keywords: Causal Effect Identification, Graphical Models, Robustness, Causal Inference

TL;DR: Results on identifying causal effects when the Markov equivalence class of the causal graph cannot be uniquely determined

Abstract: Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold.

Submission Number: 11