Poster
in
Workshop: Scaling Up Intervention Models
Towards Robust Causal Effect Identification beyond Markov Equivalence
Kai Teh · Kayvan Sadeghi · Terry Soo
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.
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