Replication Data for: Estimation and Inference on Nonlinear and Heterogeneous 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:'While multiple regression offers transparency, interpretability, and desirable theoreticalproperties, the method’s simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level partial effect, or “slope,” of a treatment variable on an outcome, and allows this value to vary with background covariates. Importantly, we introduce a robust approach to uncertainty estimates. Specifically, we combine a split-sample and conformal strategy to fit a confidence band around the partial effect curv' . 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" : ["Conformal inferece","Heterogeneous treatment effects","Machine learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'While multiple regression offers transparency, interpretability, and desirable theoreticalproperties, the method’s simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level partial effect, or “slope,” of a treatment variable on an outcome, and allows this value to vary with background covariates. Importantly, we introduce a robust approach to uncertainty estimates. Specifically, we combine a split-sample and conformal strategy to fit a confidence band around the partial effect curv' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
While multiple regression offers transparency, interpretability, and desirable theoreticalproperties, the method’s simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level partial effect, or “slope,” of a treatment variable on an outcome, and allows this value to vary with background covariates. Importantly, we introduce a robust approach to uncertainty estimates. Specifically, we combine a split-sample and conformal strategy to fit a confidence band around the partial effect curv
Conformal inferece; Heterogeneous treatment effects; Machine learning