2 citations · 5 across the 8 of their papers we have counts for
11 papers
Empirical Bayes for compound adaptive experiments
Karun Adusumilli, Jiaying Gu, Junfan Tao
We investigate Empirical Bayes (EB) methods in the context of compound adaptive experiments, where the arm distribution in each experiment follows a normal distribution with an unk…
Designing Persuasive Experiments
Karun Adusumilli, Abhi Vemulapati
Incentives in experimental design are often misaligned: experimenters design and finance experiments to seek regulatory approval, while regulators seek to maximize social welfare.…
You've Got to be Efficient: Ambiguity, Misspecification and Variational Preferences
Karun Adusumilli
We develop a framework for evaluating statistical decisions under both prior ambiguity and likelihood misspecification, which involves uniformly expanding a frequentist ambiguity s…
From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators
Karun Adusumilli, Maximilian Kasy, Ashia Wilson
We derive the asymptotic risk function of regularized empirical risk minimization (ERM) estimators tuned by -fold cross-validation (CV). The out-of-sample prediction loss of suc…
Continuous time asymptotic representations for adaptive experiments
Karun Adusumilli
This article develops a continuous-time asymptotic framework for analyzing adaptive experiments -- settings in which data collection and treatment assignment evolve dynamically in…
Optimal tests following sequential experiments
Karun Adusumilli
Recent years have seen tremendous advances in the theory and application of sequential experiments. While these experiments are not always designed with hypothesis testing in mind,…
How to sample and when to stop sampling: The generalized Wald problem and minimax policies
Karun Adusumilli
We study sequential experiments where sampling is costly and a decision-maker aims to determine the best treatment for full scale implementation by (1) adaptively allocating units…
Neyman allocation is minimax optimal for best arm identification with two arms
Karun Adusumilli
This note describes the optimal policy rule, according to the local asymptotic minimax regret criterion, for best arm identification when there are only two treatments. It is shown…
Risk and optimal policies in bandit experiments
Karun Adusumilli
We provide a decision theoretic analysis of bandit experiments under local asymptotics. Working within the framework of diffusion processes, we define suitable notions of asymptoti…
Temporal-Difference estimation of dynamic discrete choice models
Karun Adusumilli, Dita Eckardt
We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice prob…
Dynamically Optimal Treatment Allocation
Karun Adusumilli, Friedrich Geiecke, Claudio Schilter
Dynamic decisions are pivotal to economic policy making. We show how existing evidence from randomized control trials can be utilized to guide personalized decisions in challenging…