arXiv subjects

Ernesto Ulloa-Pérez

Publications and source records attributed to Ernesto Ulloa-Pérez.

3 recordsLinked to original sources

arXiv2025

Comparative Evaluation of Difference in Differences Methods for Staggered Adoption Interventions

Staggered adoption is a common approach for implementing healthcare interventions, where different units adopt the program at different times. Difference-in-differences (DiD) methods are frequently used to evaluate the effects of such interventions. Nonetheless, recent research has shown that classical DiD approaches designed for a single treatment start date can produce biased estimates in staggered adoption settings, particularly due to treatment effect heterogeneity across adoption and calendar time. Several alternative methods have been developed to address these limitations. However, these methods have not been fully systematically compared, and their practical utility remains unclear. Motivated by a payment program implemented by a healthcare provider in Hawaii, we provide a comprehensive review of the staggered adoption setting and a selection of DiD methods suitable for this context. We begin with a theoretical overview of these methods, followed by a simulation study designed to resemble the characteristics of our application, where the intervention is implemented at the cluster level. Our results show that the current methods tend to under-perform when the number of clusters is small, but improve as the number of clusters increases. We then apply the methods to evaluate the real-world payment program intervention and offer practical recommendations for researchers implementing DiD methods for staggered adoption settings. Finally, we translate our findings into practical guidance for applied researchers choosing among DiD methods for staggered adoption settings.

stat.AP↗

arXiv2024

Propensity Score Augmentation in Matching-based Estimation of Causal Effects

When assessing the causal effect of a binary exposure using observational data, confounder imbalance across exposure arms must be addressed. Matching methods, including propensity score-based matching, can be used to deconfound the causal relationship of interest. They have been particularly popular in practice, at least in part due to their simplicity and interpretability. However, these methods can suffer from low statistical efficiency compared to many competing methods. In this work, we propose a novel matching-based estimator of the average treatment effect based on a suitably-augmented propensity score model. Our procedure can be shown to have greater statistical efficiency than traditional matching estimators whenever prognostic variables are available, and in some cases, can nearly reach the nonparametric efficiency bound. In addition to a theoretical study, we provide numerical results to illustrate our findings. Finally, we use our novel procedure to estimate the effect of circumcision on risk of HIV-1 infection using vaccine efficacy trial data.

stat.ME↗

arXiv2023

Causal isotonic calibration for heterogeneous treatment effects

We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets. Cross-calibration leverages cross-fitted predictors and generates a single calibrated predictor using all available data. Under weak conditions that do not assume monotonicity, we establish that both causal isotonic calibration and cross-calibration achieve fast doubly-robust calibration rates, as long as either the propensity score or outcome regression is estimated accurately in a suitable sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm, providing robust and distribution-free calibration guarantees while preserving predictive performance.

stat.ML↗