4 papers · 1 filter
Bridging the Gap Between Design and Analysis: Randomization Inference and Sensitivity Analysis for Matched Observational Studies with Treatment Doses
Jeffrey Zhang, Siyu Heng
Matching is a commonly used causal inference study design in observational studies. Through matching on measured confounders between different treatment groups, valid randomization…
Bias Mitigation in Matched Observational Studies with Continuous Treatments: Calipered Non-Bipartite Matching and Bias-Corrected Estimation and Inference
Anthony Frazier, Siyu Heng, Wen Zhou
In matched observational studies with continuous treatments, individuals with different treatment doses but the same or similar covariate values are paired for causal inference. Wh…
Towards Robust Matched Observational Studies with General Treatment Types: Consistency, Efficiency, and Adaptivity
Siyu Heng, Elaine K. Chiu, Hyunseung Kang
To ensure reliable causal conclusions from observational studies, researchers routinely conduct sensitivity analysis to assess robustness to unmeasured confounding. In matched obse…
Sensitivity Analysis for Matched Observational Studies with Continuous Exposures and Binary Outcomes
Jeffrey Zhang, Dylan Small, Siyu Heng
Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be remov…