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math.ST2025★ 1 cited
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
Yang Cai, Alkis Kalavasis, Katerina Mamali +2
Most of the widely used estimators of the average treatment effect (ATE) in causal inference rely on the assumptions of unconfoundedness and overlap. Unconfoundedness requires that…
math.ST2024
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set . Kontonis, Tzamos, and Zampetakis (FOCS…