Search Results for author:
Found 10 papers, 0 papers with code
Exposure effects are not automatically useful for policymaking
We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists.
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.
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.
Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes
For each parameter, we provide methods for inference in an asymptotic framework where the number of clusters tends to infinity and treatment is assigned using a covariate-adaptive stratified randomization procedure.
The Local Approach to Causal Inference under Network Interference
We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network.
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials
Our main result shows that using this randomization procedure with an appropriate estimator results in an asymptotic variance which is minimal in the class of stratification trees.
Model Selection for Treatment Choice: Penalized Welfare Maximization
We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes.
Model Selection
Statistics Theory
Econometrics
Statistics Theory
