https://github.com/mattblackwell/DirectEffects
An R package for estimating controlled direct effects.
https://github.com/mattblackwell/DirectEffects
Last synced: 3 months ago
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An R package for estimating controlled direct effects.
- Host: GitHub
- URL: https://github.com/mattblackwell/DirectEffects
- Owner: mattblackwell
- Created: 2016-09-23T14:01:43.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2025-02-25T01:35:49.000Z (5 months ago)
- Last Synced: 2025-03-26T08:16:37.258Z (4 months ago)
- Language: HTML
- Homepage: https://mattblackwell.github.io/DirectEffects/
- Size: 11.7 MB
- Stars: 18
- Watchers: 6
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
---
# DirectEffects 
[](https://github.com/mattblackwell/DirectEffects/actions)
## Overview
DirectEffects is an R package to estimate controlled direct effects (CDEs), which are the effect of a treatment fixing a set of downstream mediators to particular values. As of now, the package supports general doubly robust estimation of CDEs under both selection-on-observables and difference-in-differences assumptions. sequential g-estimation and a two-stage matching approach called telescope matching. For more information on how CDEs can be useful for applied research and a brief introduction to sequential g-estimation, see [Acharya, Blackwell, and Sen (2016)][de-paper]. For more on the telescope matching procedure, see [Blackwell and Strezhnev (2022)][tm-paper]. For more on the difference-in-differences approach, see [Blackwell, Glynn, Hilbig, and Phillips (2024)][did-paper].
## Installation
You can install DirectEffects via CRAN for the current stable version or via GitHub for the development version.
```{r, eval = FALSE}
# Installing from CRAN
install.packages("DirectEffects")
# Installing development version from Github:
# install.packages("devtools")
devtools::install_github("mattblackwell/DirectEffects", build_vignettes = TRUE)
```
## Usage
DirectEffects uses a modular approach to specifying the models and estimators used to estimate the average controlled direct effect (ACDE). You can specify a propensity score and outcome regression model for each treatment variable (that is, each causal variable of interest), along with a set of parametric and machine learning estimators for these nuisance functions. The package has a workflow for [models that assume the selection-on-observables](https://mattblackwell.github.io/DirectEffects/articles/DirectEffects.html) and for models that utilize a [difference-in-differences approach](https://mattblackwell.github.io/DirectEffects/articles/did_cde.html).
```{r}
library(DirectEffects)
data(jobcorps)
my_aipw <- cde_aipw() |>
set_treatment(treat, ~ female + age_cat) |>
treat_model(engine = "logit") |>
outreg_model(engine = "lm") |>
set_treatment(work2year2q, ~ emplq4 + pemplq4) |>
treat_model(engine = "logit") |>
outreg_model(engine = "lm") |>
estimate(exhealth30 ~ treat + work2year2q, data = jobcorps)
broom::tidy(my_aipw)
```
## Specific estimator implementations
DirectEffects also implements a number of specific estimators outside the modular framework. These are often specific estimators proposed in the literature:
- [`sequential_g()`](https://mattblackwell.github.io/DirectEffects/articles/sequential_g.html): estimate controlled direct effects using two-stage linear models.
- [`telescope_match()`](https://mattblackwell.github.io/DirectEffects/articles/telescope_matching.html): estimated controlled direct effects using a two-stage matching procedure with bias correction.
DirectEffects also provides diagnostics for these two approaches, including sensitivity analyses and balance checks.
[de-paper]: http://www.mattblackwell.org/files/papers/direct-effects.pdf
[tm-paper]: https://www.mattblackwell.org/files/papers/telescope_matching.pdf
[did-paper]: https://www.mattblackwell.org/files/papers/did_cde.pdf