6 papers
Randomization Inference with Sample Attrition
Xinran Li, Peizan Sheng, Zeyang Yu
Randomization inference is a widely-used and appealing approach for analyzing treatment effects in randomized experiments, as it is finite-sample valid and does not require any dis…
Design-based nested instrumental variable analysis
Zhe Chen, Xinran Li, Michael O. Harhay +1
Two binary instrumental variables (IVs) are nested if individuals who comply under one binary IV also comply under the other. This situation often arises when the two IVs represent…
Enhanced inference for distributions and quantiles of individual treatment effects in various experiments
Zhe Chen, Xinran Li
Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to…
Cluster-robust inference with a single treated cluster using the t-test
Chun Pong Lau, Xinran Li
This paper considers inference when there is a single treated cluster and a fixed number of control clusters, a setting that is common in empirical work, especially in difference-i…
Robust Sensitivity Analysis via Augmented Percentile Bootstrap under Simultaneous Violations of Unconfoundedness and Overlap
Han Cui, Xinran Li
The identification of causal effects in observational studies typically relies on two standard assumptions: unconfoundedness and overlap. However, both assumptions are often questi…
Asymptotic Theory of the Best-Choice Rerandomization using the Mahalanobis Distance
Yuhao Wang, Xinran Li
Rerandomization, a design that utilizes pretreatment covariates and improves their balance between different treatment groups, has received attention recently in both theory and pr…