`WeightIt`

is a one-stop package to generate balancing
weights for point and longitudinal treatments in observational studies.
Contained within `WeightIt`

are methods that call on other R
packages to estimate weights. The value of `WeightIt`

is in
its unified and familiar syntax used to generate the weights, as each of
these other packages have their own, often challenging to navigate,
syntax. `WeightIt`

extends the capabilities of these packages
to generate weights used to estimate the ATE, ATT, ATC, and other
estimands for binary or multinomial treatments, and treatment effects
for continuous treatments when available. In these ways,
`WeightIt`

does for weighting what `MatchIt`

has
done for matching, and `MatchIt`

users will find the syntax
familiar.

For a complete vignette, see the website
for `WeightIt`

or `vignette("WeightIt")`

.

To install and load `WeightIt`

, use the code below:

```
#CRAN version
install.packages("WeightIt")
#Development version
::install_github("ngreifer/WeightIt")
remotes
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 ATE:

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

```
A weightit object
- method: "ps" (propensity score weighting)
- number of obs.: 614
- sampling weights: none
- treatment: 2-category
- estimand: ATE
- covariates: age, educ, nodegree, married, race, re74, re75
```

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:

```
library("cobalt")
bal.tab(W, un = TRUE)
```

```
Call
weightit(formula = treat ~ age + educ + nodegree + married +
race + re74 + re75, data = lalonde, method = "ps", estimand = "ATE")
Balance Measures
Type Diff.Un Diff.Adj
prop.score Distance 1.7569 0.1360
age Contin. -0.2419 -0.1676
educ Contin. 0.0448 0.1296
nodegree Binary 0.1114 -0.0547
married Binary -0.3236 -0.0944
race_black Binary 0.6404 0.0499
race_hispan Binary -0.0827 0.0047
race_white Binary -0.5577 -0.0546
re74 Contin. -0.5958 -0.2740
re75 Contin. -0.2870 -0.1579
Effective sample sizes
Control Treated
Unadjusted 429. 185.
Adjusted 329.01 58.33
```

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

, as demonstrated below.

`summary(W)`

```
Summary of weights
- Weight ranges:
Min Max
treated 1.1721 |---------------------------| 40.0773
control 1.0092 |-| 4.7432
- Units with 5 most extreme weights by group:
68 116 10 137 124
treated 13.5451 15.9884 23.2967 23.3891 40.0773
597 573 381 411 303
control 4.0301 4.0592 4.2397 4.5231 4.7432
- Weight statistics:
Coef of Var MAD Entropy # Zeros
treated 1.478 0.807 0.534 0
control 0.552 0.391 0.118 0
- Effective Sample Sizes:
Control Treated
Unweighted 429. 185.
Weighted 329.01 58.33
```

Desirable qualities include small coefficients of variation close to 0 and large effective sample sizes.

The table below contains the available methods in
`WeightIt`

for estimating weights for binary, multinomial,
and continuous treatments using various methods and functions from
various packages. See `vignette("installing-packages")`

for
information on how to install these packages.

Treatment type | Method (`method =` ) |
Package |
---|---|---|

Binary |
Binary regression PS (`"ps"` ) |
various |

- | Generalized boosted modeling PS (`"gbm"` ) |
`gbm` |

- | Covariate Balancing PS (`"cbps"` ) |
`CBPS` |

- | Non-Parametric Covariate Balancing PS (`"npcbps"` ) |
`CBPS` |

- | Entropy Balancing (`"ebal"` ) |
- |

- | Empirical Balancing Calibration Weights (`"ebcw"` ) |
`ATE` |

- | Optimization-Based Weights (`"optweight"` ) |
`optweight` |

- | SuperLearner PS (`"super"` ) |
`SuperLearner` |

- | Bayesian additive regression trees PS (`"bart"` ) |
`dbarts` |

- | Energy Balancing (`"energy"` ) |
- |

Multinomial |
Multinomial regression PS (`"ps"` ) |
various |

- | Generalized boosted modeling PS (`"gbm"` ) |
`gbm` |

- | Covariate Balancing PS (`"cbps"` ) |
`CBPS` |

- | Non-Parametric Covariate Balancing PS (`"npcbps"` ) |
`CBPS` |

- | Entropy Balancing (`"ebal"` ) |
- |

- | Empirical Balancing Calibration Weights (`"ebcw"` ) |
`ATE` |

- | Optimization-Based Weights (`"optweight"` ) |
`optweight` |

- | SuperLearner PS (`"super"` ) |
`SuperLearner` |

- | Bayesian additive regression trees PS (`"bart"` ) |
`dbarts` |

- | Energy Balancing (`"energy"` ) |
- |

Continuous |
Generalized linear model GPS (`"ps"` ) |
- |

- | Generalized boosted modeling GPS (`"gbm"` ) |
`gbm` |

- | Covariate Balancing GPS (`"cbps"` ) |
`CBPS` |

- | Non-Parametric Covariate Balancing GPS (`"npcbps"` ) |
`CBPS` |

- | Entropy Balancing (`"ebal"` ) |
- |

- | Optimization-Based Weights (`"optweight"` ) |
`optweight` |

- | SuperLearner GPS (`"super"` ) |
`SuperLearner` |

- | Bayesian additive regression trees GPS (`"bart"` ) |
`dbarts` |

In addition, `WeightIt`

implements the subgroup balancing
propensity score using the function `sbps()`

. Several other
tools and utilities are available.

Please submit bug reports or other issues to https://github.com/ngreifer/WeightIt/issues. If you
would like to see your package or method integrated into
`WeightIt`

, or for any other questions or comments about
`WeightIt`

, please contact the author. Fan mail is greatly
appreciated.