We show that the Augmented IPW estimator's simple t-statistics can remain well-calibrated even when strict overlap fails and there are many propensities near zero.
The large literature on vertical market bargaining assumes contracts last for one period, but actual hospital-insurer contracts last for multiple years and are negotiated as a multiple of a benchmark price that changes over time. I study dynamic regulations to those benchmark prices by extending the existing single-period approach to vertical market bargaining to enable contracts that are formed at different times.
We extend Dorn and Guo (2022)'s characterization of bounds for Tan's marginal sensitivity model to considerably more general assumptions and estimands.
We propose a meta-learner for conditional average treatment effect (CATE) bounds that can efficiently estimate sharp bounds.
*Accept with minor revisions, Journal of the American Statistical Association.* We propose semiparametric average treatment effect (ATE) bounds estimators with novel robustness properties: double sharpness and double validity.
We provide sharp bounds for an existing sensitivity analysis that are valid under minimal conditions and sharp given a consistent quantile regression estimate.