Replication Data for: Adaptive Experimental Design: Prospects and Applications in Political Science
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Experimental researchers in political science frequently face the problem of inferring which of several treatment arms is most effective. They may also seek to estimate mean outcomes under that arm, construct confidence intervals, and test hypotheses. Ordinarily, multi-arm trials conducted using static designs assign participants to each arm with fixed probabilities. However, a growing statistical literature suggests that adaptive experimental designs that dynamically allocate larger assignment probabilities to more promising treatments are better equipped to discover the best-performing arm. Using simulations and empirical applications, we explore the conditions under which such designs hasten the discovery of superior treatments  and improve the precision with which their effects are estimated. Recognizing that many scholars seek to a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Adaptive design","Experimental design","Machine learning","Multi-armed bandits","Thompson sampling"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Experimental researchers in political science frequently face the problem of inferring which of several treatment arms is most effective. They may also seek to estimate mean outcomes under that arm, construct confidence intervals, and test hypotheses. Ordinarily, multi-arm trials conducted using static designs assign participants to each arm with fixed probabilities. However, a growing statistical literature suggests that adaptive experimental designs that dynamically allocate larger assignment probabilities to more promising treatments are better equipped to discover the best-performing arm. Using simulations and empirical applications, we explore the conditions under which such designs hasten the discovery of superior treatments  and improve the precision with which their effects are estimated. Recognizing that many scholars seek to a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Experimental researchers in political science frequently face the problem of inferring which of several treatment arms is most effective. They may also seek to estimate mean outcomes under that arm, construct confidence intervals, and test hypotheses. Ordinarily, multi-arm trials conducted using static designs assign participants to each arm with fixed probabilities. However, a growing statistical literature suggests that adaptive experimental designs that dynamically allocate larger assignment probabilities to more promising treatments are better equipped to discover the best-performing arm. Using simulations and empirical applications, we explore the conditions under which such designs hasten the discovery of superior treatments  and improve the precision with which their effects are estimated. Recognizing that many scholars seek to a
Adaptive design; Experimental design; Machine learning; Multi-armed bandits; Thompson sampling