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SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
[article]
2022
arXiv
pre-print
A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure. For example, Bayesian approaches have been developed for analyzing instrumental variable designs, regression discontinuity designs, and within-subjects designs. This paper introduces simulation-based identifiability (SBI), a procedure for testing the identifiability of queries in Bayesian causal inference approaches that are implemented as probabilistic
arXiv:2102.11761v2
fatcat:5jq345dnr5fetcgsqiecpcyi7u