Preview build on GitHub Pages. The open registry will live at e2er.org.
Skip to content
Demonstrator · items marked Example are invented · what exists today
E2ER

Open datasets · Harvard Dataverse

Replication Data for Evaluation of More Doctors Program in Brazil

By Leila Amorim · as credited by the source

Published 2021-12-27 · Datasets for manuscript "Causal measures using generalized difference-in-difference approach with nonlinear models"

Deposited on Harvard Dataverse doi:10.7910/DVN/JU2ARJ licence: see repository 3 files
Open on Harvard Dataverse Use in a run Cite Suggest a correction

About this dataset

Replication Data for Analyses for Application (2) Primary Health Care and Avoidable Hospitalizations in the manuscript "Causal measures using generalized difference-in-difference approach with nonlinear models"

Collection: Datasets for manuscript "Causal measures using generalized difference-in-difference approach with nonlinear models"

Causal effectsDifference-in-difference

Use it in a run

Download the files into your project's data/ folder and start a template. The data stay on your machine; the published research records the DOI, so the authors are credited.

e2er run "<your research question>"

Automatic download by DOI is planned.

Cite

@dataset{amorim2021,
  title     = {Replication Data for Evaluation of More Doctors Program in Brazil},
  author    = {Amorim, Leila},
  year      = {2021},
  publisher = {Harvard Dataverse},
  doi       = {10.7910/DVN/JU2ARJ},
  url       = {https://doi.org/10.7910/DVN/JU2ARJ}
}

Research that uses it

No published research on E2ER uses this dataset yet.

Description

Data model
Discipline
MathematicsBiomedical
Method family
Replication
Design
not specified
Research stage
Data acquisition
Contributors
Leila Amorim (Data curation)
Usage
not used in published research yet
Source
Harvard Dataverse · Datasets for manuscript "Causal measures using generalized difference-in-difference approach with nonlinear models"
Record
dataset:dataverse/10-7910-dvn-ju2arj · JSON

Solid tags are declared by the source or mapped from its terms; dashed tags are inferred by a published rule. Hover a tag for its provenance.