ATbounds

Bounding Treatment Effects by Pooling Limited Information across Observations

https://github.com/atbounds/atbounds-r

Science Score: 20.0%

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  • CITATION.cff file
  • codemeta.json file
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  • DOI references
  • Academic publication links
    Links to: arxiv.org
  • Committers with academic emails
    1 of 1 committers (100.0%) from academic institutions
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  • Scientific vocabulary similarity
    Low similarity (14.8%) to scientific vocabulary

Keywords

causal-inference lack-of-overlap limited-overlap partial-identification treatment-effects unconfoundedness-assumption
Last synced: 10 months ago · JSON representation

Repository

Bounding Treatment Effects by Pooling Limited Information across Observations

Basic Info
  • Host: GitHub
  • Owner: ATbounds
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 3.99 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
causal-inference lack-of-overlap limited-overlap partial-identification treatment-effects unconfoundedness-assumption
Created almost 6 years ago · Last pushed over 4 years ago
Metadata Files
Readme

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ATbounds


[![Build Status](https://app.travis-ci.com/ATbounds/ATbounds-r.svg?branch=master)](https://app.travis-ci.com/ATbounds/ATbounds-r)
[![](https://cranlogs.r-pkg.org/badges/ATbounds)](https://cran.r-project.org/package=ATbounds)
[![codecov](https://codecov.io/gh/ATbounds/ATbounds-r/branch/master/graph/badge.svg?token=9GV0I2HGCV)](https://app.codecov.io/gh/ATbounds/ATbounds-r)


___ATbounds___ is an R package that provides estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. The bounds are designed to be robust in challenging situations, for example, when the the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. 

For more details, see Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," available at [arXiv:2111.05243 [econ.EM]](https://arxiv.org/abs/2111.05243).

## Installation

You can install the released version of ciccr from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("ATbounds")
```

Alternatively, you can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools") if devtools is not installed
devtools::install_github("https://github.com/ATbounds/ATbounds-r")
```

## Examples

See the vignette for case studies using real datasets. 

GitHub Events

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Last synced: over 2 years ago

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  • Total Commits: 66
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  • Avg Commits per committer: 66.0
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Top Committers
Name Email Commits
Simon Lee s****1@c****u 66
Committer Domains (Top 20 + Academic)

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Last synced: 11 months ago

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  • Average comments per issue: 0
  • Average comments per pull request: 0.6
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Top Authors
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  • sokbae (5)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 327 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: ATbounds

Bounding Treatment Effects by Limited Information Pooling

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 327 Last month
Rankings
Stargazers count: 26.2%
Forks count: 28.8%
Dependent packages count: 29.8%
Average: 35.4%
Dependent repos count: 35.5%
Downloads: 56.7%
Maintainers (1)
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • mgcv * imports
  • stats * imports
  • ggplot2 * suggests
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests