Replication Data for: Does Regression Produce Representative Estimates of Causal Effects?
<|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:'With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest.  What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample.  Causal effects estimated via multiple regression differentially weight each unit's contribution.  The ``effective sample'' that regression uses to generate the estimate may bear little resemblance to the population of interest, and the results may be nonrepresentative in a manner similar to what quasi-experimental methods or experiments with convenience samples produce.  There is no general external validity basis for preferring multiple regression on representative samples over quasi-experimental or experimental methods.  We show how to ' . 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","External validity","Multiple regression","Observational studies","Randomized experiments"]} <|eot_id|>
 Here is the Description of a dataset. Description:'With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest.  What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample.  Causal effects estimated via multiple regression differentially weight each unit's contribution.  The ``effective sample'' that regression uses to generate the estimate may bear little resemblance to the population of interest, and the results may be nonrepresentative in a manner similar to what quasi-experimental methods or experiments with convenience samples produce.  There is no general external validity basis for preferring multiple regression on representative samples over quasi-experimental or experimental methods.  We show how to ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest.  What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample.  Causal effects estimated via multiple regression differentially weight each unit's contribution.  The ``effective sample'' that regression uses to generate the estimate may bear little resemblance to the population of interest, and the results may be nonrepresentative in a manner similar to what quasi-experimental methods or experiments with convenience samples produce.  There is no general external validity basis for preferring multiple regression on representative samples over quasi-experimental or experimental methods.  We show how to 
Causal inference; External validity; Multiple regression; Observational studies; Randomized experiments