weightit: Estimate Balancing Weights

In WeightIt: Weighting for Covariate Balance in Observational Studies

Estimate Balancing Weights

Description

weightit() allows for the easy generation of balancing weights using a variety of available methods for binary, continuous, and multi-category treatments. Some of these methods require functions in other packages, which weightit() calls; these packages must be installed to use the desired method.

Usage

weightit(
  formula,
  data = NULL,
  method = "glm",
  estimand = "ATE",
  stabilize = FALSE,
  focal = NULL,
  by = NULL,
  s.weights = NULL,
  ps = NULL,
  missing = NULL,
  verbose = FALSE,
  include.obj = FALSE,
  keep.mparts = TRUE,
  ...
)

Arguments

formula	a formula with a treatment variable on the left hand side and the covariates to be balanced on the right hand side. See glm() for more details. Interactions and functions of covariates are allowed.
data	an optional data set in the form of a data frame that contains the variables in formula.
method	a string of length 1 containing the name of the method that will be used to estimate weights. See Details below for allowable options. The default is "glm" for propensity score weighting using a generalized linear model to estimate the propensity score.
estimand	the desired estimand. For binary and multi-category treatments, can be "ATE", "ATT", "ATC", and, for some methods, "ATO", "ATM", or "ATOS". The default for both is "ATE". This argument is ignored for continuous treatments. See the individual pages for each method for more information on which estimands are allowed with each method and what literature to read to interpret these estimands.
stabilize	whether or not and how to stabilize the weights. If TRUE, each unit's weight will be multiplied by a stabilization factor, which is the the unconditional probability (or density) of each unit's observed treatment value. If a formula, a generalized linear model will be fit with the included predictors, and the inverse of the corresponding weight will be used as the standardization factor. Can only be used when estimand = "ATE" or with continuous treatments. Default is FALSE for no stabilization. See also the num.formula argument at weightitMSM(). For continuous treatments, weights are already stabilized, so setting stabilize = TRUE will be ignored with a warning (supplying a formula still works).
focal	when estimand is set to "ATT" or "ATC", which group to consider the "treated" or "control" group, respectively. This group will not be weighted, and the other groups will be weighted to resemble the focal group. If specified, estimand will automatically be set to "ATT" (with a warning if estimand is not "ATT" or "ATC"). See section estimand and focal in Details below.
by	a string containing the name of the variable in data for which weighting is to be done within categories or a one-sided formula with the stratifying variable on the right-hand side. For example, if by = "gender" or by = ~gender, a separate propensity score model or optimization will occur within each level of the variable "gender". Only one by variable is allowed; to stratify by multiply variables simultaneously, create a new variable that is a full cross of those variables using interaction().
s.weights	an optional vector of sampling weights or the name of a variable in data that contains sampling weights. See the individual pages for each method for information on whether sampling weights can be supplied.
ps	an optional vector of propensity scores or the name of a variable in data containing propensity scores. If supplied, method is ignored unless it is a user-supplied function, and the propensity scores will be used to create weights. formula must include the treatment variable in data, but the listed covariates will play no role in the weight estimation. Using ps is similar to calling get_w_from_ps() directly, but produces a full weightit object rather than just producing weights.
missing	character; how missing data should be handled. The options and defaults depend on the method used. Ignored if no missing data is present. It should be noted that multiple imputation outperforms all available missingness methods available in weightit() and should probably be used instead. Consider the MatchThem package for the use of weightit() with multiply imputed data.
verbose	logical; whether to print additional information output by the fitting function. Default is FALSE to suppress output.
include.obj	logical; whether to include in the output any fit objects created in the process of estimating the weights. For example, with method = "glm", the glm objects containing the propensity score model will be included. See the individual pages for each method for information on what object will be included if TRUE. Default is FALSE to keep the returned object small.
keep.mparts	logical; whether to include in the output components necessary to estimate standard errors that account for estimation of the weights in glm_weightit(). Default is TRUE if such parts are present. See the individual pages for each method for whether these components are produced. Set to FALSE to keep the output object smaller, e.g., if standard errors will not be computed using glm_weightit().
...	other arguments for functions called by weightit() that control aspects of fitting that are not covered by the above arguments. See Details.

Details

The primary purpose of weightit() is as a dispatcher to functions that perform the estimation of balancing weights using the requested method. Below are the methods allowed and links to pages containing more information about them, including additional arguments and outputs (e.g., when include.obj = TRUE), how missing values are treated, which estimands are allowed, and whether sampling weights are allowed.

method	Name
"glm"	Propensity score weighting using generalized linear models
"gbm"	Propensity score weighting using generalized boosted modeling
"cbps"	Covariate Balancing Propensity Score weighting
"npcbps"	Non-parametric Covariate Balancing Propensity Score weighting
"ebal"	Entropy balancing
"ipt"	Inverse probability tilting
"optweight"	Stable balancing weights
"super"	Propensity score weighting using SuperLearner
"bart"	Propensity score weighting using Bayesian additive regression trees (BART)
"energy"	Energy balancing
"cfd"	Characteristic function distance balancing

method can also be supplied as a user-defined function; see method_user for instructions and examples. Setting method = NULL computes unit weights.

Empty model formulas

The right hand side of formula may be empty, as in A ~ 1, requesting a marginal model in which the treatment (or censoring) is taken to be independent of the covariates. With no covariates there is nothing for any method to model or balance, and every method's target is met by the same weights: the inverse of the marginal treatment probability, or 1/P(C = 0) for a censoring model. Those weights are computed by fitting an intercept-only generalized linear model whatever method is supplied, which is simply the easiest way to get them; any method-specific arguments are ignored, since none of them can apply to covariates that do not exist. This is invisible: method is reported as supplied, the package for the requested method need not be installed, and the weights are what that method would have produced. Every method therefore accepts an empty formula.

For a continuous treatment the conditional density of the treatment is its marginal density, so all the weights are exactly 1 and nothing is estimated; no M-estimation components are produced. For the other treatment types the marginal probability is estimated, so M-estimation is available as usual.

estimand, focal, by, s.weights, and stabilize are unaffected, and the arguments are still checked against the requested method, so a method that cannot handle the treatment type at all (e.g., "npcbps" with a censoring model) still produces an error.

A formula whose only terms are lme4-style random effects, such as A ~ (1 | school), is not empty in this sense and is fit as the multilevel model it describes.

Censoring weights (IPCW)

Wrapping the left side of formula in .cens() requests inverse probability of censoring weights instead of treatment weights, as in weightit(.cens(C) ~ x1 + x2, data = d, method = "glm"). Censoring is treated as its own treatment type, distinct from binary, multi-category, and continuous treatments; the indicator must be 0 or FALSE for units still under observation and 1 or TRUE for units that are censored.

Weights are estimated only for the units still under observation, and are those that make their covariate distribution resemble that of the full at-risk sample. Writing e(X) = P(C = 1 | X), the weights are 1 / (1 - e(X)) for units with C = 0 and exactly 0 for units with C = 1. Because only one group is weighted, the estimation problem is smaller and better conditioned than the corresponding binary-treatment problem, which would additionally solve for weights among the censored units; this matters most when few units are censored.

estimand, focal, and subclass do not apply and are rejected or ignored. by and stabilize can be used; stabilization multiplies the weights by P(C = 0 | V) from a second censoring model (marginal when stabilize is TRUE, otherwise fit with the predictors in the supplied formula), giving P(C = 0 | V) / P(C = 0 | X) for the units still under observation and leaving the censored units at exactly 0. ps is the predicted probability of being censored. Not all methods support estimating censoring weights; see the treat_type component of .weightit_methods.

As with a treatment model, the right side of the formula may be empty, as in .cens(C) ~ 1, which requests a marginal censoring model that assumes censoring is independent of the covariates. The resulting weights are 1/P(C = 0) for the units still under observation and 0 for the censored units. The rest of the censoring machinery is unaffected, so such a model can be combined with by, s.weights, stabilize (which then makes all nonzero weights exactly 1), and M-estimation, and it can be interleaved with covariate-dependent censoring models in weightitMSM(). See Empty model formulas above for how method is handled.

Because censored units receive a weight of exactly 0, they contribute nothing to a weighted outcome model, and glm_weightit() and friends tolerate missing values in the model variables for those units, including a missing event time in the Surv() response of a coxph_weightit() model. Missing values in units with a nonzero weight still produce an error. See .cens() for how to assess balance, which requires a little care.

estimand and focal

For binary and multi-category treatments, the argument to estimand determines what distribution the weighted sample should resemble. When set to "ATE", this requests that each group resemble the full sample. When set to "ATO", "ATM", or "ATOS" (for the methods that allow them), this requests that each group resemble an "overlap" sample. When set to "ATT" or "ATC", this requests that each group resemble the treated or control group, respectively (termed the "focal" group). Weights are set to 1 for the focal group.

How does weightit() decide which group is the treated and which group is the control? For binary treatments, several heuristics are used. The first is by checking whether a valid argument to focal was supplied containing the name of the focal group, which is the treated group when estimand = "ATT" and the control group when estimand = "ATC". If focal is not supplied, guesses are made using the following criteria, evaluated in order:

• If the treatment variable is logical, TRUE is considered treated and FALSE control.

• If the treatment is numeric (or a string or factor with values that can be coerced to numeric values), if 0 is one of the values, it is considered the control, and otherwise, the lower value is considered the control (with the other considered treated).

• If exactly one of the treatment values is "t", "tr", "treat", "treated", or "exposed", it is considered the treated (and the other control).

• If exactly one of the treatment values is "c", "co", "ctrl", "control", or "unexposed", it is considered the control (and the other treated).

• If the treatment variable is a factor, the first level is considered control and the second treated.

• The lowest value after sorting with sort() is considered control and the other treated.

To be safe, it is best to code your binary treatment variable as 0 for control and 1 for treated. Otherwise, focal should be supplied when requesting the ATT or ATC. For multi-category treatments, focal is required when requesting the ATT or ATC; none of the heuristics above are used.

Citing WeightIt

When using weightit(), please cite both the WeightIt package (using citation("WeightIt")) and the paper(s) in the references section of the method used.

Value

A weightit object with the following elements:

weights	The estimated weights, one for each unit.
treat	The values of the treatment variable.
covs	The covariates used in the fitting. Only includes the raw covariates, which may have been altered in the fitting process.
estimand	The estimand requested.
method	The weight estimation method specified.
ps	The estimated or provided propensity scores. Estimated propensity scores are returned for binary treatments and only when method is one that estimates propensity scores. The propensity score corresponds to the predicted probability of being treated; see section estimand and focal in Details for how the treated group is determined.
s.weights	The provided sampling weights, or a vector of 1s of none are provided.
focal	The focal treatment level if the ATT or ATC was requested.
by	A data frame containing the by variable when specified.
obj	When include.obj = TRUE, the fit object.
info	Additional information about the fitting. See the individual methods pages for what is included.

When keep.mparts is TRUE (the default) and the chosen method is compatible with M-estimation, the components related to M-estimation for use in glm_weightit() are stored in the "Mparts" attribute. When by is specified, the per-stratum M-estimation components are instead combined and stored in the "Mparts.list" attribute; the resulting standard errors produced by glm_weightit() are asymptotically equivalent to those from estimating the weights from a single model in which the by variable is fully interacted with all the covariates. The same is true for weightitMSM(), where the equivalent model interacts the by variable with all the covariates at every time point.

See Also

weightitMSM() for estimating weights with sequential (i.e., longitudinal) treatments or with both treatment and censoring indicators for use in estimating marginal structural models (MSMs).

weightit.fit(), which is a lower-level dispatcher function that accepts a matrix of covariates and a vector of treatment statuses rather than a formula and data frame and performs minimal argument checking and processing. It may be useful for speeding up simulation studies for which the correct arguments are known. In general, weightit() should be used.

summary.weightit() for summarizing the distribution of the weights.

Examples

library("cobalt")
data("lalonde", package = "cobalt")

#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
                  nodegree + re74, data = lalonde,
                method = "glm", estimand = "ATT"))
summary(W1)
bal.tab(W1)

#Balancing covariates with respect to race (multi-category)
(W2 <- weightit(race ~ age + educ + married +
                  nodegree + re74, data = lalonde,
                method = "ebal", estimand = "ATE"))
summary(W2)
bal.tab(W2)

#Balancing covariates with respect to re75 (continuous)
(W3 <- weightit(re75 ~ age + educ + married +
                  nodegree + re74, data = lalonde,
                method = "cbps"))
summary(W3)
bal.tab(W3)

WeightIt documentation built on Aug. 4, 2026, 1:09 a.m.