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Found 10 papers, 2 papers with code
Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs
We introduce a multivariate local-linear estimator for multivariate regression discontinuity designs in which treatment is assigned by crossing a boundary in the space of running variables.
Adaptive Experimental Design for Policy Learning
Setting the worst-case expected regret as the performance criterion of adaptive sampling and recommended policies, we derive its asymptotic lower bounds, and propose a strategy, Adaptive Sampling-Policy Learning strategy (PLAS), whose leading factor of the regret upper bound aligns with the lower bound as the size of experimental units increases.
Bandwidth Selection for Treatment Choice with Binary Outcomes
This study considers the treatment choice problem when outcome variables are binary.
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds
We evaluate the decision based on the expected simple regret, which is the difference between the expected outcomes of the best arm and the recommended arm.
Shrinkage Methods for Treatment Choice
Then, we compare the maximum regret of the proposed shrinkage rule with that of CES and pooling rules when the parameter space is correctly specified and misspecified.
Best Arm Identification with Contextual Information under a Small Gap
We then develop the ``Random Sampling (RS)-Augmented Inverse Probability weighting (AIPW) strategy,'' which is asymptotically optimal in the sense that the probability of misidentification under the strategy matches the lower bound when the budget goes to infinity in the small-gap regime.
Joint diagnostic test of regression discontinuity designs: multiple testing problem
Current diagnostic tests for regression discontinuity (RD) design face a multiple testing problem.
Evidence Aggregation for Treatment Choice
Consider a planner who has to decide whether or not to introduce a new policy to a certain local population.
Manipulation-Robust Regression Discontinuity Designs
We present a new identification condition for regression discontinuity designs.
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
In adaptive experimental design, the experimenter is allowed to change the probability of assigning a treatment using past observations for estimating the ATE efficiently.
