WeightIt

WeightIt: an R package for propensity score weighting

https://github.com/ngreifer/weightit

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WeightIt: an R package for propensity score weighting

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = FALSE,
  warning = FALSE,
  message = FALSE,
  tidy = FALSE,
  fig.align='center',
  comment = "#>",
  fig.path = "man/figures/README-"
)
```
# WeightIt: Weighting for Covariate Balance in Observational Studies 

[![CRAN status](https://www.r-pkg.org/badges/version/WeightIt?color=00622B)](https://CRAN.R-project.org/package=WeightIt)
[![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/WeightIt?color=00622B)](https://cran.r-project.org/package=WeightIt)

### Overview

*WeightIt* is a one-stop package to generate balancing weights for point and longitudinal treatments in observational studies. Support is included for binary, multi-category, and continuous treatments, a variety of estimands including the ATE, ATT, ATC, ATO, and others, and support for a wide variety of weighting methods, including those that rely on parametric modeling, machine learning, or optimization. *WeightIt* also provides functionality for fitting regression models in weighted samples that account for estimation of the weights in quantifying uncertainty. *WeightIt* uses a familiar formula interface and is meant to complement `MatchIt` as a package that provides a unified interface to basic and advanced weighting methods.

For a complete vignette, see the [website](https://ngreifer.github.io/WeightIt/articles/WeightIt.html) for *WeightIt* or `vignette("WeightIt")`.

To install and load *WeightIt*, use the code below:

```{r, eval = FALSE}
#CRAN version
pak::pkg_install("WeightIt")

#Development version
pak::pkg_install("ngreifer/WeightIt")

library("WeightIt")
```
```{r, include = FALSE}
library("WeightIt")
```

The workhorse function of *WeightIt* is `weightit()`, which generates weights from a given formula and data input according to methods and other parameters specified by the user. Below is an example of the use of `weightit()` to generate propensity score weights for estimating the ATT:

```{r}
data("lalonde", package = "cobalt")

W <- weightit(treat ~ age + educ + nodegree + 
                married + race + re74 + re75, 
              data = lalonde, method = "glm", 
              estimand = "ATT")
W
```

Evaluating weights has two components: evaluating the covariate balance produced by the weights, and evaluating whether the weights will allow for sufficient precision in the eventual effect estimate. For the first goal, functions in the *cobalt* package, which are fully compatible with *WeightIt*, can be used, as demonstrated below:

```{r}
library("cobalt")

bal.tab(W, un = TRUE)
```

For the second goal, qualities of the distributions of weights can be assessed using `summary()`, as demonstrated below.

```{r}
summary(W)
```

Large effective sample sizes imply low variability in the weights, and therefore increased precision in estimating the treatment effect.

Finally, we can estimate the effect of the treatment using a weighted outcome model, accounting for estimation of the weights in the standard error of the effect estimate:

```{r}
fit <- lm_weightit(re78 ~ treat, data = lalonde,
                   weightit = W)

summary(fit, ci = TRUE)
```

The tables below contain the available methods in *WeightIt* for estimating weights for binary, multi-category, and continuous treatments. Some of these methods require installing other packages to use; see `vignette("installing-packages")` for information on how to install them.
 
#### Binary Treatments

 Method               | `method`
 -------------------- | --------
 Binary regression PS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)
 Generalized boosted modeling PS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)
 Covariate balancing PS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)
 Non-Parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)
 Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)
 Inverse probability tilting | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html)
 Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html)
 SuperLearner PS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)
 Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)
 Energy balancing | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)

#### Multi-Category Treatments
 
 Method               | `method`
 -------------------- | --------
 Multinomial regression PS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)
 Generalized boosted modeling PS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)
 Covariate balancing PS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)
 Non-parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)
 Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)
 Inverse probability tilting | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html)
 Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html)
 SuperLearner PS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)
 Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)
 Energy balancing | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)
 
#### Continuous Treatments
 
 Method               | `method`
 -------------------- | --------
 Generalized linear model GPS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)
 Generalized boosted modeling GPS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)
 Covariate balancing GPS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)
 Non-parametric covariate balancing GPS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)
 Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)
 Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html)
 SuperLearner GPS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)
 Bayesian additive regression trees GPS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)
 Distance covariance optimal weighting | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)
 
In addition, *WeightIt* implements the subgroup balancing propensity score using the function `sbps()`. Several other tools and utilities are available, including `trim()` to trim or truncate weights, `calibrate()` to calibrate propensity scores, and `get_w_from_ps()` to compute weights from propensity scores.

*WeightIt* provides functions to fit weighted models that account for the uncertainty in estimating the weights. These include `glm_weightit()` for fitting generalized linear models, `ordinal_weightit()` for ordinal regression models, `multinom_weightit()` for multinomial regression models, and `coxph_weightit()` for Cox proportional hazards models. Several methods are available for computing the parameter variances, including asymptotically correct M-estimation-based variances, robust variances that treat the weights as fixed, and traditional and fractional weighted bootstrap variances. Clustered variances are supported. See `vignette("estimating-effects")` for information on how to use these after weighting to estimate treatment effects.

Please submit bug reports, questions, comments, or other issues to https://github.com/ngreifer/WeightIt/issues. If you would like to see your package or method integrated into *WeightIt*, please contact the author. Fan mail is greatly appreciated.

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