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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 for Two-stage Experiments under Covariate-Adaptive Randomization
Finally, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator.
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.
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.
Learning Intuitive Policies Using Action Features
An unaddressed challenge in multi-agent coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations.
Zero-Shot Coordination via Semantic Relationships Between Actions and Observations
An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations.
