Replication Data for: Front-door Difference-in-Differences Estimators
<|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:'We develop front-door difference-in-differences estimators as an extension of front-door estimators. Under one-sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows estimation when the front-door criterion does not hold. Even if the assumptions are relaxed, we show that the front-door and front-door difference-in-differences estimators may be combined to form bounds. Finally, we show that under one-sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida’s early in-person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early' . 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","Job training","Voting"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We develop front-door difference-in-differences estimators as an extension of front-door estimators. Under one-sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows estimation when the front-door criterion does not hold. Even if the assumptions are relaxed, we show that the front-door and front-door difference-in-differences estimators may be combined to form bounds. Finally, we show that under one-sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida’s early in-person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We develop front-door difference-in-differences estimators as an extension of front-door estimators. Under one-sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows estimation when the front-door criterion does not hold. Even if the assumptions are relaxed, we show that the front-door and front-door difference-in-differences estimators may be combined to form bounds. Finally, we show that under one-sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida’s early in-person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early
Causal inference; Job training; Voting