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Difference-in-differences with stochastic policy shifts of a continuous treatment
Michael Jetsupphasuk, Chenwei Fang, Didong Li +1
Treatment effects of stochastic policy shifts quantify differences in outcomes across counterfactual scenarios with varying treatment distributions. Stochastic policy shifts may be…
Efficient nonparametric estimation with difference-in-differences in the presence of network dependence and interference
Michael Jetsupphasuk, Didong Li, Michael G. Hudgens
Differences-in-differences (DiD) is a causal inference method for observational longitudinal data that assumes parallel expected potential outcome trajectories between treatment gr…
Nonparametric Causal Survival Analysis with Clustered Interference
Chanhwa Lee, Donglin Zeng, Michael Emch +2
Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additi…
Assessing Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Justin B. DeMonte, Bonnie E. Shook-Sa, Michael G. Hudgens
Observational data are frequently used to evaluate real-world vaccine effectiveness (VE). For vaccines such as those developed against COVID-19, VE may vary over calendar time beca…
Finite sample performance of optimal treatment rule estimators with right-censored outcomes
Michael Jetsupphasuk, Michael G. Hudgens, Jessie K. Edwards +1
Patient care may be improved by recommending treatments based on patient characteristics when there is treatment effect heterogeneity. Recently, there has been a great deal of atte…
Exposure Effects on Count Outcomes with Observational Data, with Application to Incarcerated Women
Bonnie E. Shook-Sa, Michael G. Hudgens, Andrea K. Knittel +14
Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the…