Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.
2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS) , pages=
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Towards a holistic understanding of Selection Bias for Causal Effect Identification
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.