Replication Data for: When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal 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:'Many researchers use unit fixed effects regression models as their default methods for causal inference with longitudinal data.  We show that the ability of these models to adjust for unobserved time-invariant confounders comes at the expense of dynamic causal relationships, which are permitted under an alternative selection-on-observables approach.  Using the nonparametric directed acyclic graph, we highlight two key causal identification assumptions of unit fixed effects models: past treatments do not directly influence current outcome, and past outcomes do not affect current treatment.  Furthermore, we introduce a new nonparametric matching framework that elucidates how various unit fixed effects models implicitly compare treated and control observations to draw causal inference.  By establishing the equivalence between matching and ' . 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" : ["Directed acyclic graph","Panel data"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Many researchers use unit fixed effects regression models as their default methods for causal inference with longitudinal data.  We show that the ability of these models to adjust for unobserved time-invariant confounders comes at the expense of dynamic causal relationships, which are permitted under an alternative selection-on-observables approach.  Using the nonparametric directed acyclic graph, we highlight two key causal identification assumptions of unit fixed effects models: past treatments do not directly influence current outcome, and past outcomes do not affect current treatment.  Furthermore, we introduce a new nonparametric matching framework that elucidates how various unit fixed effects models implicitly compare treated and control observations to draw causal inference.  By establishing the equivalence between matching and ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Many researchers use unit fixed effects regression models as their default methods for causal inference with longitudinal data.  We show that the ability of these models to adjust for unobserved time-invariant confounders comes at the expense of dynamic causal relationships, which are permitted under an alternative selection-on-observables approach.  Using the nonparametric directed acyclic graph, we highlight two key causal identification assumptions of unit fixed effects models: past treatments do not directly influence current outcome, and past outcomes do not affect current treatment.  Furthermore, we introduce a new nonparametric matching framework that elucidates how various unit fixed effects models implicitly compare treated and control observations to draw causal inference.  By establishing the equivalence between matching and 
Directed acyclic graph; Panel data