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stat.ME2023
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…
stat.ME2023
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…