A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances
The past two decades have witnessed a surge of new research in the analysis of
randomized experiments. The emergence of this literature may seem surprising given the …
randomized experiments. The emergence of this literature may seem surprising given the …
The transfer performance of economic models
Economists often estimate models using data from a particular domain, eg estimating risk
preferences in a particular subject pool or for a specific class of lotteries. Whether a model's …
preferences in a particular subject pool or for a specific class of lotteries. Whether a model's …
Model-robust and efficient covariate adjustment for cluster-randomized experiments
Cluster-randomized experiments are increasingly used to evaluate interventions in routine
practice conditions, and researchers often adopt model-based methods with covariate …
practice conditions, and researchers often adopt model-based methods with covariate …
On the efficiency of finely stratified experiments
This paper studies the efficient estimation of a large class of treatment effect parameters that
arise in the analysis of experiments. Here, efficiency is understood to be with respect to a …
arise in the analysis of experiments. Here, efficiency is understood to be with respect to a …
Inference in cluster randomized trials with matched pairs
This paper considers the problem of inference in cluster randomized trials where treatment
status is determined according to a" matched pairs''design. Here, by a cluster randomized …
status is determined according to a" matched pairs''design. Here, by a cluster randomized …
Non-robustness of the cluster-robust inference: with a proposal of a new robust method
Y Sasaki, Y Wang - arXiv preprint arXiv:2210.16991, 2022 - arxiv.org
The conventional cluster-robust (CR) standard errors may not be robust. They are
vulnerable to data that contain a small number of large clusters. When a researcher uses the …
vulnerable to data that contain a small number of large clusters. When a researcher uses the …
Inference for two-stage experiments under covariate-adaptive randomization
J Liu - arXiv preprint arXiv:2301.09016, 2023 - arxiv.org
This paper studies inference in two-stage randomized experiments under covariate-adaptive
randomization. In the initial stage of this experimental design, clusters (eg, households …
randomization. In the initial stage of this experimental design, clusters (eg, households …
[PDF][PDF] On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It
HD Chiang, Y Sasaki, Y Wang - arXiv preprint arXiv …, 2023 - economics.sas.upenn.edu
Conventional methods of cluster-robust inference are inconsistent in the presence of
unignorably large clusters. We formalize this claim by establishing a necessary and …
unignorably large clusters. We formalize this claim by establishing a necessary and …
[PDF][PDF] The University of Chicago
H Liu - United States, 2016 - knowledge.uchicago.edu
This paper studies inference in randomized controlled trials with multiple treatments, where
treatment status is determined according to a “matched tuples” design. If there are| D …
treatment status is determined according to a “matched tuples” design. If there are| D …
Design-based estimation theory for complex experiments
H Chang - arXiv preprint arXiv:2311.06891, 2023 - arxiv.org
This paper considers the estimation of treatment effects in randomized experiments with
complex experimental designs, including cases with interference between units. We develop …
complex experimental designs, including cases with interference between units. We develop …