Source Themes

Why Are Share of Charges Contracts Long-Lived?

I study auto-renew share of charges contracts, which are associated with small insurers paying high prices to American hospitals. I demonstrate that under certain conditions, these contracts can lead to Pareto improvements. The key feature of these contracts is the renewal process, which enables the insurers to discipline hospital charges with the threat of contract termination and renegotiation.

How Much Overlap Failure Can Doubly-Robust T-Statistics Handle?

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.

Dynamic Bargaining between Hospitals and Insurers (Job Market Paper)

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.

Six Stylized Facts From Ten Years of Vertical Market Contract Data

I investigate vertical market contract dynamics by documenting and analyzing a novel panel dataset of hospital--insurer contracts in West Virginia. The largest insurer typically formed three- and five-year contracts. In contrast, smaller insurers generally formed long-lived contracts with faster price growth. By documenting a unique dataset and stark dynamic implications, this research contributes to a larger understanding of vertical market dynamics and helps set the stage for future work.

Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding

We propose semiparametric average treatment effect (ATE) bounds estimators with novel robustness properties: double sharpness and double validity.

Sensitivity Analysis for Linear Estimators

We extend Dorn and Guo (2022)'s characterization of bounds for Tan's marginal sensitivity model to considerably more general assumptions and estimands.

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

We propose a meta-learner for conditional average treatment effect (CATE) bounds that can efficiently estimate sharp bounds.

Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing

We provide sharp bounds for an existing sensitivity analysis that are valid under minimal conditions and sharp given a consistent quantile regression estimate.