Type:	Package
Title:	Optimization-Based Stable Balancing Weights
Version:	1.0.0
Description:	Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.
Depends:	R (≥ 4.1.0)
Imports:	osqp (≥ 0.6.3.3), chk (≥ 0.10.0), rlang (≥ 1.1.6), Matrix (≥ 1.2-13), ggplot2 (≥ 3.5.0), graphics, stats, utils
Suggests:	cobalt (≥ 4.6.0), scs (≥ 3.2.7), clarabel (≥ 0.10.1), highs (≥ 1.10.0-3), lpSolve (≥ 5.6.23), WeightIt, gbm, marginaleffects, sandwich, fwb, knitr, rmarkdown, testthat (≥ 3.0.0)
License:	GPL-2 | GPL-3 [expanded from: GPL]
Encoding:	UTF-8
URL:	https://ngreifer.github.io/optweight/, https://github.com/ngreifer/optweight
BugReports:	https://github.com/ngreifer/optweight/issues
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-09-08 22:44:12 UTC; NoahGreifer
Author:	Noah Greifer [aut, cre]
Maintainer:	Noah Greifer <noah.greifer@gmail.com>
Repository:	CRAN
Date/Publication:	2025-09-09 06:50:11 UTC

optweight: Optimization-Based Stable Balancing Weights

Description

Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) doi:10.1080/01621459.2015.1023805. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.

Author(s)

Maintainer: Noah Greifer noah.greifer@gmail.com (ORCID)

See Also

Useful links:

• https://ngreifer.github.io/optweight/

• https://github.com/ngreifer/optweight

• Report bugs at https://github.com/ngreifer/optweight/issues

Estimate Stable Balancing Weights

Description

Estimate stable balancing weights for treatments and covariates specified in formula. The degree of balance for each covariate is specified by tols and the target population can be specified with targets or estimand. See Zubizarreta (2015) and Wang & Zubizarreta (2019) for details of the properties of the weights and the methods used to fit them.

Usage

optweight(
  formula,
  data = NULL,
  tols = 0,
  estimand = "ATE",
  targets = NULL,
  s.weights = NULL,
  b.weights = NULL,
  focal = NULL,
  norm = "l2",
  verbose = FALSE,
  ...
)

optweightMV(
  formula.list,
  data = NULL,
  tols.list = list(0),
  estimand = "ATE",
  targets = NULL,
  s.weights = NULL,
  b.weights = NULL,
  focal = NULL,
  norm = "l2",
  verbose = FALSE,
  ...
)

Arguments

Details

The optimization is performed by the lower-level function optweight.fit() (for optweight()) or optweightMV.fit() (for optweightMV()).

For binary and multi-category treatments, weights are estimated so that the weighted mean differences of the covariates are within the given tolerance thresholds (unless std.binary or std.cont are TRUE, in which case standardized mean differences are considered for binary and continuous variables, respectively). For a covariate x with specified tolerance \delta, the weighted means of each each group will be within \delta of each other. Additionally, when the ATE is specified as the estimand or a target population is specified, the weighted means of each group will each be within \delta/2 of the target means; this ensures generalizability to the same population from which the original sample was drawn.

If standardized tolerance values are requested, the standardization factor corresponds to the estimand requested: when the ATE is requested or a target population specified, the standardization factor is the square root of the average variance for that covariate across treatment groups, and when the ATT or ATC are requested, the standardization factor is the standard deviation of the covariate in the focal group. The standardization factor is computed accounting for s.weights.

For continuous treatments, weights are estimated so that the weighted correlation between the treatment and each covariate is within the specified tolerance threshold. If the ATE is requested or a target population is specified, the means of the weighted covariates and treatment are restricted to be equal to those of the target population to ensure generalizability to the desired target population. The weighted correlation is computed as the weighted covariance divided by the product of the unweighted standard deviations. The means used to center the variables in computing the covariance are those specified in the target population.

Dual Variables

Two types of constraints may be associated with each covariate: target constraints and balance constraints. Target constraints require the mean of the covariate to be at (or near) a specific target value in each treatment group (or for the whole group when treatment is continuous). Balance constraints require the means of the covariate in pairs of treatments to be near each other. For binary and multi-category treatments, balance constraints are redundant if target constraints are provided for a variable. For continuous variables, balance constraints refer to the correlation between treatment and the covariate and are not redundant with target constraints. In the duals component of the output, each covariate has a dual variable for each nonredundant constraint placed on it.

The dual variable for each constraint is the instantaneous rate of change of the objective function at the optimum corresponding to a change in the constraint. Because this relationship is not linear, large changes in the constraint will not exactly map onto corresponding changes in the objective function at the optimum, but will be close for small changes in the constraint. For example, for a covariate with a balance constraint of .01 and a corresponding dual variable of 40, increasing (i.e., relaxing) the constraint to .025 will decrease the value of the objective function at the optimum by approximately (.025 - .01) * 40 = .6.

For factor variables, optweight() takes the sum of the absolute dual variables for the constraints for all levels and reports it as the the single dual variable for the variable itself. This summed dual variable works the same way as dual variables for continuous variables do.

Value

For optweight(), an optweight object with the following elements:

For optweightMV(), an optweightMV object with the following elements:

References

Chattopadhyay, A., Cohn, E. R., & Zubizarreta, J. R. (2024). One-Step Weighting to Generalize and Transport Treatment Effect Estimates to a Target Population. The American Statistician, 78(3), 280–289. doi:10.1080/00031305.2023.2267598

Källberg, D., & Waernbaum, I. (2023). Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects. Econometrics and Statistics. doi:10.1016/j.ecosta.2023.11.004

Wang, Y., & Zubizarreta, J. R. (2020). Minimal dispersion approximately balancing weights: Asymptotic properties and practical considerations. Biometrika, 107(1), 93–105. doi:10.1093/biomet/asz050

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. doi:10.1080/01621459.2015.1023805

See Also

optweight.fit(), the lower-level function that performs the fitting. Links on that page can help with diagnosing and fixing more subtle issues with the optimization.

sbw, which was the inspiration for this package and provides some additional functionality for binary treatments.

WeightIt, which provides a simplified interface to optweight() and a more efficient implementation of entropy balancing.

Examples

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

# Balancing covariates between treatment groups (binary)
(ow1 <- optweight(treat ~ age + educ + married +
                    nodegree + re74, data = lalonde,
                  tols = c(.01, .02, .03, .04, .05),
                  estimand = "ATE"))
bal.tab(ow1)

# Exactly alancing covariates with respect to race (multi-category)
(ow2 <- optweight(race ~ age + educ + married +
                    nodegree + re74, data = lalonde,
                  tols = 0, estimand = "ATT",
                  focal = "black"))
bal.tab(ow2)

# Balancing covariates between treatment groups (binary)
# and requesting a specified target population
targets <- process_targets(~ age + educ + married +
                             nodegree + re74,
                           data = lalonde,
                           targets = c(26, 12, .4, .5,
                                       1000))

(ow3a <- optweight(treat ~ age + educ + married +
                     nodegree + re74, data = lalonde,
                   targets = targets,
                   estimand = NULL))

bal.tab(ow3a, disp.means = TRUE)

# Balancing covariates between treatment groups (binary)
# and not requesting a target population
(ow3b <- optweight(treat ~ age + educ + married +
                     nodegree + re74, data = lalonde,
                   targets = NULL,
                   estimand = NULL))

bal.tab(ow3b, disp.means = TRUE)

# Balancing two treatments
(ow4 <- optweightMV(list(treat ~ age + educ + race + re74,
                         re75 ~ age + educ + race + re74),
                    data = lalonde))

summary(ow4)

bal.tab(ow4)

# Using a different norm
(ow1b <- optweight(treat ~ age + educ + married +
                    nodegree + re74, data = lalonde,
                  tols = c(.01, .02, .03, .04, .05),
                  estimand = "ATE",
                  norm = "l1"))

summary(ow1b, weight.range = FALSE)
summary(ow1, weight.range = FALSE)

# Allowing for negative weights
ow5 <- optweight(treat ~ age + educ + married + race +
                   nodegree + re74 + re75,
                 data = lalonde,
                 estimand = "ATE",
                 min.w = -Inf)

summary(ow5)

Fitting Function for Stable Balancing Weights

Description

optweight.fit() and optweightMV.fit() perform the optimization for optweight() and optweightMV() and should, in most cases, not be used directly. Little processing of inputs is performed, so they must be given exactly as described below.

Usage

optweight.fit(
  covs,
  treat,
  tols = 0,
  estimand = "ATE",
  targets = NULL,
  s.weights = NULL,
  b.weights = NULL,
  focal = NULL,
  norm = "l2",
  std.binary = FALSE,
  std.cont = TRUE,
  min.w = 1e-08,
  verbose = FALSE,
  solver = NULL,
  ...
)

optweightMV.fit(
  covs.list,
  treat.list,
  tols.list = list(0),
  estimand = "ATE",
  targets = NULL,
  s.weights = NULL,
  b.weights = NULL,
  norm = "l2",
  std.binary = FALSE,
  std.cont = TRUE,
  min.w = 1e-08,
  verbose = FALSE,
  solver = NULL,
  ...
)

Arguments

Details

optweight.fit() and optweightMV.fit() transform the inputs into the required inputs for the optimization functions, which are (sparse) matrices and vectors, and then supplies the outputs (the weights, dual variables, and convergence information) back to optweight() or optweightMV(). Little processing of inputs is performed, as this is normally handled by optweight() or optweightMV().

Target and balance constraints are applied to the product of the estimated weights and the sampling weights. In addition,the sum of the product of the estimated weights and the sampling weights is constrained to be equal to the sum of the product of the base weights and sampling weights. For binary and multi-category treatments, these constraints apply within each treatment group.

norm

The objective function for the optimization problem is f\left(w_i, b_i, s_i\right), where w_i is the estimated weight for unit i, s_i is the sampling weight for unit i (supplied by s.weights) and b_i is the base weight for unit i (supplied by b.weights). The norm argument determines f(.,.,.), as detailed below:

• when norm = "l2", f\left(w_i, b_i, s_i\right) = \frac{1}{n} \sum_i {s_i(w_i - b_i)^2}

• when norm = "l1", f\left(w_i, b_i, s_i\right) = \frac{1}{n} \sum_i {s_i \vert w_i - b_i \vert}

• when norm = "linf", f\left(w_i, b_i, s_i\right) = \max_i {\vert w_i - b_i \vert}

• when norm = "entropy", f\left(w_i, b_i, s_i\right) = \frac{1}{n} \sum_i {s_i w_i \log \frac{w_i}{b_i}}

• when norm = "log", f\left(w_i, b_i, s_i\right) = \frac{1}{n} \sum_i {-s_i \log \frac{w_i}{b_i}}

By default, s.weights and b.weights are set to 1 for all units unless supplied. b.weights must be positive when norm is "entropy" or "log", and norm = "linf" cannot be used when s.weights are supplied.

When norm = "l2" and both s.weights and b.weights are NULL, weights are estimated to maximize the effective sample size. When norm = "entropy", the estimated weights are equivalent to entropy balancing weights (Källberg & Waernbaum, 2023). When norm = "log", b.weights are ignored in the optimization, as they do not affect the estimated weights.

solver

The solver argument controls which optimization solver is used. Different solvers are compatible with each norm. See the table below for allowable options, which package they require, which function does the solving, and which function controls the settings.

Note that "lpsolve" can only be used when min.w is nonnegative.

The default solver for each norm is as follows:

If the package corresponding to a default solver is not installed but the package for a different eligible solver is, that will be used. Otherwise, you will be asked to install the required package. osqp is required for optweight, and so will be the default for the "l1" and "linf" norms if highs is not installed. The default package is the one has shown good performance for the given norm; generally, all eligible solvers perform about equally well in terms of accuracy but differ in time taken.

Solving Convergence Failure

Sometimes the optimization will fail to converge at a solution. There are a variety of reasons why this might happen, which include that the constraints are nearly impossible to satisfy or that the optimization surface is relatively flat. It can be hard to know the exact cause or how to solve it, but this section offers some solutions one might try. Typically, solutions can be found most easily when using the "l2" norm; other norms, especially "linf" and "l1", are more likely to see problems.

Rarely is the problem too few iterations, though this is possible. Most problems can be solved in the default 200,000 iterations, but sometimes it can help to increase this number with the max_iter argument. Usually, though, this just ends up taking more time without a solution found.

If the problem is that the constraints are too tight, it can be helpful to loosen the constraints. Sometimes examining the dual variables of a solution that has failed to converge can reveal which constraints are causing the problem.

Sometimes a suboptimal solution is possible; such a solution does not satisfy the constraints exactly but will come pretty close. To allow these solutions, the argument eps can be increased to larger values.

Sometimes using a different solver can improve performance. Using the default solver for each norm, as described above, can reduce the probability of convergence failures.

Value

An optweight.fit or optweightMV.fit object with the following elements:

References

Chattopadhyay, A., Cohn, E. R., & Zubizarreta, J. R. (2024). One-Step Weighting to Generalize and Transport Treatment Effect Estimates to a Target Population. The American Statistician, 78(3), 280–289. doi:10.1080/00031305.2023.2267598

Källberg, D., & Waernbaum, I. (2023). Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects. Econometrics and Statistics. doi:10.1016/j.ecosta.2023.11.004

Wang, Y., & Zubizarreta, J. R. (2020). Minimal dispersion approximately balancing weights: Asymptotic properties and practical considerations. Biometrika, 107(1), 93–105. doi:10.1093/biomet/asz050

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. doi:10.1080/01621459.2015.1023805

See Also

optweight() and optweightMV() which you should use for estimating the balancing weights, unless you know better.

Examples

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

treat <- lalonde$treat
covs <- splitfactor(lalonde[2:8], drop.first = "if2")

ow.fit <- optweight.fit(covs,
                        treat,
                        tols = .02,
                        estimand = "ATE",
                        norm = "l2")

Estimate Targeting Weights Using Optimization

Description

Estimate targeting weights for covariates specified in formula. The target means are specified with targets and the maximum distance between each weighted covariate mean and the corresponding target mean is specified by tols. See Zubizarreta (2015) for details of the properties of the weights and the methods used to fit them.

Usage

optweight.svy(
  formula,
  data = NULL,
  tols = 0,
  targets = NULL,
  s.weights = NULL,
  b.weights = NULL,
  norm = "l2",
  verbose = FALSE,
  ...
)

Arguments

Details

The optimization is performed by the lower-level function optweight.svy.fit().

Weights are estimated so that the standardized differences between the weighted covariate means and the corresponding targets are within the given tolerance thresholds (unless std.binary or std.cont are FALSE, in which case unstandardized mean differences are considered for binary and continuous variables, respectively). For a covariate x with specified tolerance \delta, the weighted mean will be within \delta of the target. If standardized tolerance values are requested, the standardization factor is the standard deviation of the covariate in the whole sample. The standardization factor is always unweighted.

See the optweight() help page for information on interpreting dual variables and solving convergence failure.

Value

An optweight.svy object with the following elements:

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. doi:10.1080/01621459.2015.1023805

See Also

optweight.svy.fit(), the lower-level function that performs the fitting.

optweight.fit() for more details about the optimization options.

optweight() for estimating weights that balance treatment groups.

Examples

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

cov.formula <- ~ age + educ + race + married + nodegree

targets <- process_targets(cov.formula, data = lalonde,
                           targets = c(23, 9, .3, .3, .4,
                                       .2, .5))

ows <- optweight.svy(cov.formula,
                     data = lalonde,
                     tols = 0,
                     targets = targets)
ows

#Unweighted means
col_w_mean(ows$covs)

#Weighted means; same as targets
col_w_mean(ows$covs, w = ows$weights)

Fitting Function for Optweight for Survey Weights

Description

optweight.svy.fit() performs the optimization for optweight.svy() and should, in most cases, not be used directly. Little processing of inputs is performed, so they must be given exactly as described below.

Usage

optweight.svy.fit(
  covs,
  targets,
  tols = 0,
  s.weights = NULL,
  b.weights = NULL,
  norm = "l2",
  std.binary = FALSE,
  std.cont = TRUE,
  min.w = 1e-08,
  verbose = FALSE,
  solver = NULL,
  ...
)

Arguments

Details

optweight.svy.fit() transforms the inputs into the required inputs for the optimization functions, which are (sparse) matrices and vectors, and then supplies the outputs (the weights, dual variables, and convergence information) back to optweight.svy(). Little processing of inputs is performed, as this is normally handled by optweight.svy().

Target constraints are applied to the product of the estimated weights and the sampling weights. In addition, sum of the product of the estimated weights and the sampling weights is constrained to be equal to the sum of the product of the base weights and sampling weights.

Value

An optweight.svy.fit object with the following elements:

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. doi:10.1080/01621459.2015.1023805

See Also

optweight.svy() which you should use for estimating the balancing weights, unless you know better.

optweight.fit() for more details about the allowed norms and optimization.

Examples

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

covs <- splitfactor(lalonde[c("age", "educ", "race",
                              "married", "nodegree")],
                    drop.first = FALSE)

targets <- c(23, 9, .3, .3, .4, .2, .5)

ows.fit <- optweight.svy.fit(covs,
                             targets = targets,
                             norm = "l2")

#Unweighted means
col_w_mean(covs)

#Weighted means; same as targets
col_w_mean(covs, w = ows.fit$w)

Plot Dual Variables for Assessing Balance Constraints

Description

Plots the dual variables resulting from optweight() in a way similar to figure 2 of Zubizarreta (2015), which explained how to interpret these values. These represent the cost of changing the constraint on the variance of the resulting weights. For covariates with large values of the dual variable, tightening the constraint will increase the variability of the weights, and loosening the constraint will decrease the variability of the weights, both to a greater extent than would doing the same for covariate with small values of the dual variable.

Usage

## S3 method for class 'optweight'
plot(x, ...)

## S3 method for class 'optweightMV'
plot(x, which.treat = 1, ...)

## S3 method for class 'optweight.svy'
plot(x, ...)

Arguments

Value

A ggplot object that can be used with other ggplot2 functions.

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. doi:10.1080/01621459.2015.1023805

See Also

optweight(), optweightMV(), or optweight.svy() to estimate the weights and the dual variables

plot.summary.optweight() for plots of the distribution of weights

Examples

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

tols <- process_tols(treat ~ age + educ + married +
                       nodegree + re74, data = lalonde,
                     tols = .1)

#Balancing covariates between treatment groups (binary)
ow1 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow1) # Note the RMSE Dev and effective
#              sample size (ESS)

plot(ow1) # age has a low value, married is high

tols["age"] <- 0
ow2 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow2) # Notice that tightening the constraint
#              on age had a negligible effect on the
#              variability of the weights and ESS

tols["age"] <- .1
tols["married"] <- 0
ow3 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

summary(ow3) # In contrast, tightening the constraint
#              on married had a large effect on the
#              variability of the weights, shrinking
#              the ESS

Construct and Check Targets Input

Description

Checks whether proposed target population means values for targets are suitable in number and order for submission to optweight() and optweight.svy(). Users should include one value per variable in formula. For factor variables, one value per level of the variable is required. The output of process_targets() can also be used as an input to targets in optweight() and optweight.svy().

Usage

process_targets(formula, data = NULL, targets = NULL, s.weights = NULL)

check.targets(...)

## S3 method for class 'optweight.targets'
print(x, digits = 5, ...)

Arguments

Details

The purpose of process_targets() is to allow users to ensure that their proposed input to targets in optweight() and optweight.svy() is correct both in the number of entries and their order. This is especially important when factor variables and interactions are included in the formula because factor variables are split into several dummies and interactions are moved to the end of the variable list, both of which can cause some confusion and potential error when entering targets values.

Factor variables are internally split into a dummy variable for each level, so the user must specify a target population mean value for each level of the factor. These must add up to 1, and an error will be displayed if they do not. These values represent the proportion of units in the target population with each factor level.

Interactions (e.g., a:b or a*b in the formula input) are always sent to the end of the variable list even if they are specified elsewhere in the formula. It is important to run process_targets() to ensure the order of the proposed targets corresponds to the represented order of covariates used in the formula. You can run process_targets(., targets = NA) to see the order of covariates that is required without specifying any targets.

Value

An optweight.targets object, which is a named vector of target population mean values, one for each (expanded) covariate specified in formula. This should be used as user inputs to optweight() and optweight.svy().

See Also

process_tols()

Examples

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

# Generating targets; means by default
targets <- process_targets(~ age + race + married +
                             nodegree + re74,
                           data = lalonde)

# Notice race is split into three values
targets

# Generating targets; NA by default
targets <- process_targets(~ age + race + married +
                             nodegree + re74,
                           data = lalonde,
                           targets = NA)
targets

# Can also supply just a dataset
covs <- lalonde |>
  subset(select = c(age, race, married,
                    nodegree, re74))

targets <- process_targets(covs)

targets

Construct and Check Tolerance Input

Description

Checks whether proposed tolerance values for tols are suitable in number and order for submission to optweight(). Users should include one value per item in formula. The output can also be used as an input to tols in optweight().

Usage

process_tols(formula, data = NULL, tols = 0)

check.tols(...)

## S3 method for class 'optweight.tols'
print(x, internal = FALSE, digits = 5, ...)

Arguments

Details

The purpose of process_tols() is to allow users to ensure that their proposed input to tols in optweight() is correct both in the number of entries and their order. This is especially important when factor variables and interactions are included in the formula because factor variables are split into several dummies and interactions are moved to the end of the variable list, both of which can cause some confusion and potential error when entering tols values.

Factor variables are internally split into a dummy variable for each level, but the user only needs to specify one tolerance value per original variable; process_tols() automatically expands the tols input to match the newly created variables.

Interactions (e.g., a:b or a*b in the formula input) are always sent to the end of the variable list even if they are specified elsewhere in the formula. It is important to run process_tols() to ensure the order of the proposed tols corresponds to the represented order of covariates used in optweight(). You can run process_tols() with no tols input to see the order of covariates that is required.

process_tols() was designed to be used primarily for its message printing and print() method, but you can also assign its output to an object for use as an input to tols in optweight().

Note that only one formula and vector of tolerance values can be assessed at a time; for multiple treatments, each formula and tolerance vector must be entered separately.

Value

An optweight.tols object, which is a named vector of tolerance values, one for each variable specified in formula. This should be used as user inputs to optweight(). The "internal.tols" attribute contains the tolerance values to be used internally by optweight(). These will differ from the vector values when there are factor variables that are split up; the user only needs to submit one tolerance per factor variable, but separate tolerance values are produced for each new dummy created.

See Also

process_targets()

Examples

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

# Generating tols; 0 by default
tols <- process_tols(treat ~ age + educ + married +
                       nodegree + re74,
                     data = lalonde)

tols

tols <- process_tols(treat ~ age + educ + married +
                       nodegree + re74,
                     data = lalonde,
                     tols = .05)

tols

# Checking the order of interactions; notice they go
# at the end even if specified at the beginning.
tols <- process_tols(treat ~ age:educ + married*race +
                       nodegree + re74,
                     data = lalonde,
                     tols = .05)

tols

# Internal tolerances for expanded covariates
print(tols, internal = TRUE)

Summarize, print, and plot information about estimated weights

Description

These functions summarize the weights resulting from a call to optweight() or optweight.svy(). summary() produces summary statistics on the distribution of weights, including their range and variability, and the effective sample size of the weighted sample (computing using the formula in McCaffrey, Rudgeway, & Morral, 2004). plot() creates a histogram of the weights.

Usage

## S3 method for class 'optweight'
summary(object, top = 5, ignore.s.weights = FALSE, weight.range = TRUE, ...)

## S3 method for class 'optweightMV'
summary(object, top = 5, ignore.s.weights = FALSE, weight.range = TRUE, ...)

## S3 method for class 'optweight.svy'
summary(object, top = 5, ignore.s.weights = FALSE, weight.range = TRUE, ...)

## S3 method for class 'summary.optweight'
plot(x, ...)

Arguments

Value

For point treatments (i.e., optweight objects), summary() returns a summary.optweight object with the following elements:

For multivariate treatments (i.e., optweightMV objects), a list of the above elements for each treatment.

For optweight.svy objects, a list of the above elements but with no treatment group divisions.

plot() returns a ggplot object with a histogram displaying the distribution of the estimated weights. If the estimand is the ATT or ATC, only the weights for the non-focal group(s) will be displayed (since the weights for the focal group are all 1). A dotted line is displayed at the mean of the weights (the mean of the base weights, or 1 if not supplied).

References

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies. Psychological Methods, 9(4), 403–425. doi:10.1037/1082-989X.9.4.403

See Also

plot.optweight() for plotting the values of the dual variables.

Examples

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

#Balancing covariates between treatment groups (binary)
(ow1 <- optweight(treat ~ age + educ + married +
                    nodegree + re74, data = lalonde,
                  tols = .001,
                  estimand = "ATT"))

(s <- summary(ow1))

plot(s, breaks = 12)