8 papers
A Universal Framework for Factorial Matched Observational Studies with General Treatment Types: Design, Analysis, and Applications
Jianan Zhu, Tianruo Zhang, Diana Silver +4
Matching is one of the most widely used causal inference frameworks in observational studies. However, all the existing matching-based causal inference methods are designed for eit…
A Non-Bipartite Matching Framework for Difference-in-Differences with General Treatment Types
Siyu Heng, Yuan Huang, Hyunseung Kang
Difference-in-differences (DID) is one of the most widely used causal inference frameworks in observational studies. However, most existing DID methods are designed for binary trea…
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…
Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
Siyu Heng, Jiawei Zhang, Yang Feng
Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to t…
Randomization-Based Inference for Average Treatment Effects in Inexactly Matched Observational Studies
Jianan Zhu, Jeffrey Zhang, Zijian Guo +1
Matching is a widely used causal inference design that aims to approximate a randomized experiment using observational data by forming matched sets of treated and control units bas…