Replication data for: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation
<|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:'A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals — such as households, communities, firms, medical practices, schools, or classrooms — even when the individual is the unit of interest. To recoup some of the resulting efficiency loss, many studies pair similar clusters and randomize treatment within pairs. Other studies (including almost all published political science field experiments) avoid pairing, in part because some prominent methodological articles claim to have identified serious problems with this “matched-pair cluster-randomized” design. We prove that all such claims about problems with this design are unfounded. We then show that the estimator for matched-pair designs favored in the literature is appropriate only in situations where matching is not needed. 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" : ["causal inference, community intervention trials, field experiments, group-randomized trials, health policy, matched-pair design, noncompliance"]} <|eot_id|>
 Here is the Description of a dataset. Description:'A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals — such as households, communities, firms, medical practices, schools, or classrooms — even when the individual is the unit of interest. To recoup some of the resulting efficiency loss, many studies pair similar clusters and randomize treatment within pairs. Other studies (including almost all published political science field experiments) avoid pairing, in part because some prominent methodological articles claim to have identified serious problems with this “matched-pair cluster-randomized” design. We prove that all such claims about problems with this design are unfounded. We then show that the estimator for matched-pair designs favored in the literature is appropriate only in situations where matching is not needed. To a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals — such as households, communities, firms, medical practices, schools, or classrooms — even when the individual is the unit of interest. To recoup some of the resulting efficiency loss, many studies pair similar clusters and randomize treatment within pairs. Other studies (including almost all published political science field experiments) avoid pairing, in part because some prominent methodological articles claim to have identified serious problems with this “matched-pair cluster-randomized” design. We prove that all such claims about problems with this design are unfounded. We then show that the estimator for matched-pair designs favored in the literature is appropriate only in situations where matching is not needed. To a
causal inference, community intervention trials, field experiments, group-randomized trials, health policy, matched-pair design, noncompliance