`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 CRAN page for `WeightIt`

.

To install and load `WeightIt`

, use the code below:

```
install.packages("WeightIt") #CRAN version
devtools::install_github("ngreifer/WeightIt") #Development version
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")
W <- weightit(treat ~ age + educ + nodegree + married + race + re74 + re75, data = lalonde,
method = "ps", estimand = "ATE")
print(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 produces 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:

```
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 429.000 185.000
Adjusted 329.008 58.327
```

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

, as demonstrated below.

```
Summary of weights:
- Weight ranges:
Min Max
treated 1.1721 |---------------------------| 40.0773
control 1.0092 |-| 4.7432
- Units with 5 greatest weights by group:
137 124 116 68 10
treated 13.5451 15.9884 23.2967 23.3891 40.0773
597 573 411 381 303
control 4.0301 4.0592 4.2397 4.5231 4.7432
Ratio Coef of Var
treated 34.1921 1.4777
control 4.7002 0.5519
overall 39.7134 1.3709
- Effective Sample Sizes:
Control Treated
Unweighted 429.000 185.000
Weighted 329.008 58.327
```

Desirable qualities include ratios close to 1, 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.

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

Binary |
Binary regression PS (`"ps"` ) |
`glm()` |
`base` |

- | Generalized boosted modeling PS (`"gbm"` /`"twang"` ) |
`gbm.fit()` /`ps()` |
`gbm` /`twang` |

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

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

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

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

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

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

Multinomial |
Multiple binary regression PS (`"ps"` ) |
`glm()` |
`base` |

- | Multinomial regression PS (`"ps"` ) |
`mlogit()` |
`mlogit` |

- | Bayesian multinomial regression PS (`"ps", link = "bayes.probit"` ) |
`MNP()` |
`MNP` |

- | Generalized boosted modeling PS (`"gbm"` /`"twang"` ) |
`gbm.fit()` /`mnps()` |
`gbm` /`twang` |

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

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

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

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

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

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

Continuous |
Generalized linear model PS (`"ps"` ) |
`glm()` |
`base` |

- | Generalized boosted modeling PS (`"gbm"` /`"twang"` ) |
`gbm.fit()` /`ps.cont()` |
`gbm` /`WeightIt` |

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

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

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

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

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 Noah Greifer at noah.greifer@gmail.com. Fan mail is greatly appreciated.