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Found 7 papers, 0 papers with code
On the Efficiency of Finely Stratified Experiments
By a "finely stratified" design, we mean experiments in which units are divided into groups of a fixed size and a proportion within each group is assigned to treatment uniformly at random so that it respects the restriction on the marginal probability of treatment assignment.
Inference in Experiments with Matched Pairs and Imperfect Compliance
To this end, we derive the limiting behavior of a two-stage least squares estimator of the local average treatment effect which includes both the additional covariates in addition to pair fixed effects, and show that the limiting variance is always less than or equal to that of the Wald estimator.
Covariate Adjustment in Experiments with Matched Pairs
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision.
Inference in Cluster Randomized Trials with Matched Pairs
Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the cluster; by a "matched pairs'' design we mean that a sample of clusters is paired according to baseline, cluster-level covariates and, within each pair, one cluster is selected at random for treatment.
Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition
To address these claims, we derive the estimands obtained from the difference-in-means estimator in a matched-pair design both when the observations from pairs with an attrited unit are retained and when they are dropped.
Optimality of Matched-Pair Designs in Randomized Controlled Trials
In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization.
Inference for Matched Tuples and Fully Blocked Factorial Designs
Leveraging our previous results, we establish that our estimator achieves a lower asymptotic variance under the fully-blocked design than that under any stratified factorial design which stratifies the experimental sample into a finite number of "large" strata.
