Replication Data for: Matching Methods for Causal Inference with Time-Series Cross-Section Data
<|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:'Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics.  While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data.  We fill this methodological gap.  In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags.  We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values.  Assessing the quality of matches is done by examining covariate balance.  Finally, we estimate both short-term and long-term average treatment effects' . 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" : ["Difference-in-differences","Fixed effects","Observational studies","Unobserved confounding","Weighting"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics.  While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data.  We fill this methodological gap.  In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags.  We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values.  Assessing the quality of matches is done by examining covariate balance.  Finally, we estimate both short-term and long-term average treatment effects' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics.  While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data.  We fill this methodological gap.  In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags.  We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values.  Assessing the quality of matches is done by examining covariate balance.  Finally, we estimate both short-term and long-term average treatment effects
Difference-in-differences; Fixed effects; Observational studies; Unobserved confounding; Weighting