B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

2 code implementations • 20 Apr 2023 • Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data.

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