This page explains the details of estimating weights using energy balancing by setting method = "energy" in the call to weightit() or weightitMSM(). This method can be used with binary, multi-category, and continuous treatments.
In general, this method relies on estimating weights by minimizing an energy statistic related to covariate balance. For binary and multi-category treatments, this is the energy distance, which is a multivariate distance between distributions, between treatment groups. For continuous treatments, this is the sum of the distance covariance between the treatment variable and the covariates and the energy distances between the treatment and covariates in the weighted sample and their distributions in the original sample. This method relies on code written for WeightIt using osqposqp from the osqp package to perform the optimization. This method may be slow or memory-intensive for large datasets.
Binary Treatments
For binary treatments, this method estimates the weights using osqp() using formulas described by Huling and Mak (2024). The following estimands are allowed: ATE, ATT, and ATC.
Multi-Category Treatments
For multi-category treatments, this method estimates the weights using osqp() using formulas described by Huling and Mak (2024). The following estimands are allowed: ATE and ATT.
Continuous Treatments
For continuous treatments, this method estimates the weights using osqp() using formulas described by Huling, Greifer, and Chen (2023).
Longitudinal Treatments
For longitudinal treatments, the weights are the product of the weights estimated at each time point. This method is not guaranteed to yield optimal balance at each time point. NOTE: the use of energy balancing with longitudinal treatments has not been validated!
Sampling Weights
Sampling weights are supported through s.weights in all scenarios. In some cases, sampling weights will cause the optimization to fail due to lack of convexity or infeasible constraints.
Missing Data
In the presence of missing data, the following value(s) for missing are allowed:
"ind"(default)First, for each variable with missingness, a new missingness indicator variable is created which takes the value 1 if the original covariate is
NAand 0 otherwise. The missingness indicators are added to the model formula as main effects. The missing values in the covariates are then replaced with the covariate medians (this value is arbitrary and does not affect estimation). The weight estimation then proceeds with this new formula and set of covariates. The covariates output in the resultingweightitobject will be the original covariates with theNAs.
Details
Energy balancing is a method of estimating weights using optimization without a propensity score. The weights are the solution to a constrain quadratic optimization problem where the objective function concerns covariate balance as measured by the energy distance and (for continuous treatments) the distance covariance.
Energy balancing for binary and multi-category treatments involves minimizing the energy distance between the treatment groups and between each treatment group and a target group (e.g., the full sample for the ATE). The energy distance is a scalar measure of the difference between two multivariate distributions and is equal to 0 when the two distributions are identical.
Energy balancing for continuous treatments involves minimizing the distance covariance between the treatment and the covariates; the distance covariance is a scalar measure of the association between two (possibly multivariate) distributions that is equal to 0 when the two distributions are independent. In addition, the energy distances between the treatment and covariate distributions in the weighted sample and the treatment and covariate distributions in the original sample are minimized.
The primary benefit of energy balancing is that all features of the covariate distribution are balanced, not just means, as with other optimization-based methods like entropy balancing. Still, it is possible to add additional balance constraints to require balance on individual terms using the moments argument, just like with entropy balancing. Energy balancing can sometimes yield weights with high variability; the lambda argument can be supplied to penalize highly variable weights to increase the effective sample size at the expense of balance.
Reproducibility
Although there are no stochastic components to the optimization, a feature turned off by default is to update the optimization based on how long the optimization has been running, which will vary across runs even when a seed is set and no parameters have been changed. See the discussion here for more details. To ensure reproducibility by default, adaptive_rho_interval is set to 10. See osqposqpSettings for details.
Note
Sometimes the optimization can fail to converge because the problem is not convex. A warning will be displayed if so. In these cases, try simply re-fitting the weights without changing anything (but see the Reproducibility section above). If the method repeatedly fails, you should try another method or change the supplied parameters (though this is uncommon). Increasing max_iter or changing adaptive_rho_interval might help.
If it seems like the weights are balancing the covariates but you still get a failure to converge, this usually indicates that more iterations are needs to find the optimal solutions. This can occur when moments or int are specified. max_iter should be increased, and setting verbose = TRUE allows you to monitor the process and examine if the optimization is approaching convergence.
Additional Arguments
The following following additional arguments can be specified:
dist.matthe name of the method used to compute the distance matrix of the covariates or the numeric distance matrix itself. Allowable options include
"scaled_euclidean"for the Euclidean (L2) distance on the scaled covariates (the default),"mahalanobis"for the Mahalanobis distance, and"euclidean"for the raw Euclidean distance. Abbreviations allowed. Note that some user-supplied distance matrices can cause the R session to abort due to a bug within osqp, so this argument should be used with caution. A distance matrix must be a square, symmetric, numeric matrix with zeros along the diagonal and a row and column for each unit. Can also be supplied as the output of a call todist().lambdaa positive numeric scalar used to penalize the square of the weights. This value divided by the square of the total sample size is added to the diagonal of the quadratic part of the loss function. Higher values favor weights with less variability. Note this is distinct from the lambda value described in Huling and Mak (2024), which penalizes the complexity of individual treatment rules rather than the weights, but does correspond to lambda from Huling et al. (2023). Default is .0001, which is essentially 0.
For binary and multi-category treatments, the following additional arguments can be specified:
improvedlogical; whether to use the improved energy balancing weights as described by Huling and Mak (2024) whenestimand = "ATE". This involves optimizing balance not only between each treatment group and the overall sample, but also between each pair of treatment groups. Huling and Mak (2024) found that the improved energy balancing weights generally outperformed standard energy balancing. Default isTRUE; set toFALSEto use the standard energy balancing weights instead (not recommended).quantileA named list of quantiles (values between 0 and 1) for each continuous covariate, which are used to create additional variables that when balanced ensure balance on the corresponding quantile of the variable. For example, setting
quantile = list(x1 = c(.25, .5. , .75))ensures the 25th, 50th, and 75th percentiles ofx1in each treatment group will be balanced in the weighted sample. Can also be a single number (e.g.,.5) or an unnamed list of length 1 (e.g.,list(c(.25, .5, .75))) to request the same quantile(s) for all continuous covariates, or a named vector (e.g.,c(x1 = .5, x2 = .75)to request one quantile for each covariate.
For continuous treatments, the following additional arguments can be specified:
d.momentsThe number of moments of the treatment and covariate distributions that are constrained to be the same in the weighted sample as in the original sample. For example, setting
d.moments = 3ensures that the mean, variance, and skew of the treatment and covariates are the same in the weighted sample as in the unweighted sample.d.momentsshould be greater than or equal tomomentsand will be automatically set accordingly if not (or if not specified).dimension.adjlogical; whether to include the dimensionality adjustment described by Huling et al. (2023). IfTRUE, the default, the energy distance for the covariates is weighted \(\sqrt{p}\) times as much as the energy distance for the treatment, where \(p\) is the number of covariates. IfFALSE, the two energy distances are given equal weights. Default isTRUE.
The moments argument functions differently for method = "energy" from how it does with other methods. When unspecified or set to zero, energy balancing weights are estimated as described by Huling and Mak (2024) for binary and multi-category treatments or by Huling et al. (2023) for continuous treatments. When moments is set to an integer larger than 0, additional balance constraints on the requested moments of the covariates are also included, guaranteeing exact moment balance on these covariates while minimizing the energy distance of the weighted sample. For binary and multi-category treatments, this involves exact balance on the means of the entered covariates; for continuous treatments, this involves exact balance on the treatment-covariate correlations of the entered covariates.
Any other arguments will be passed to osqposqpSettings. Some defaults differ from those in osqpSettings(); see Reproducibility below.
Additional Outputs
objWhen
include.obj = TRUE, the output of the call to osqpsolve_osqp, which contains the dual variables and convergence information.
References
Binary and multi-category treatments
Huling, J. D., & Mak, S. (2024). Energy balancing of covariate distributions. Journal of Causal Inference, 12(1). doi:10.1515/jci-2022-0029
Continuous treatments
Huling, J. D., Greifer, N., & Chen, G. (2023). Independence weights for causal inference with continuous treatments. Journal of the American Statistical Association, 0(ja), 1–25. doi:10.1080/01621459.2023.2213485
Author
Noah Greifer, using code from Jared Huling's independenceWeights package for continuous treatments.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "energy", estimand = "ATE"))
#> A weightit object
#> - method: "energy" (energy balancing)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATE
#> - covariates: age, educ, married, nodegree, re74
summary(W1)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 0 |---------------| 7.5273
#> control 0 |---------------------------| 12.9264
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 124 183 172 184 181
#> treated 3.7973 3.9464 4.7248 5.7377 7.5273
#> 567 374 192 303 608
#> control 5.0956 5.1599 5.1633 6.5388 12.9264
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 1.148 0.852 0.551 6
#> control 1.162 0.764 0.466 4
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185.
#> Weighted 182.83 80.09
bal.tab(W1)
#> Balance Measures
#> Type Diff.Adj
#> age Contin. -0.0235
#> educ Contin. 0.0052
#> married Binary -0.0055
#> nodegree Binary 0.0006
#> re74 Contin. -0.0061
#>
#> Effective sample sizes
#> Control Treated
#> Unadjusted 429. 185.
#> Adjusted 182.83 80.09
#Balancing covariates with respect to race (multi-category)
(W2 <- weightit(race ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "energy", estimand = "ATT",
focal = "black"))
#> A weightit object
#> - method: "energy" (energy balancing)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 3-category (black, hispan, white)
#> - estimand: ATT (focal: black)
#> - covariates: age, educ, married, nodegree, re74
summary(W2)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> black 1 || 1.0000
#> hispan 0 |---------| 4.6858
#> white 0 |---------------------------| 12.6742
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 6 5 4 3 1
#> black 1 1 1 1 1
#> 28 269 373 570 424
#> hispan 3.6372 3.9319 4.1098 4.5448 4.6858
#> 76 422 96 42 140
#> white 7.6424 7.6594 9.3907 11.1661 12.6742
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> black 0.000 0.000 0.000 0
#> hispan 1.165 0.879 0.618 2
#> white 1.733 1.073 0.966 79
#>
#> - Effective Sample Sizes:
#>
#> black hispan white
#> Unweighted 243 72. 299.
#> Weighted 243 30.8 74.87
bal.tab(W2)
#> Balance summary across all treatment pairs
#> Type Max.Diff.Adj
#> age Contin. 0.0389
#> educ Contin. 0.0455
#> married Binary 0.0094
#> nodegree Binary 0.0027
#> re74 Contin. 0.0259
#>
#> Effective sample sizes
#> hispan white black
#> Unadjusted 72. 299. 243
#> Adjusted 30.8 74.87 243
# \donttest{
#Balancing covariates with respect to re75 (continuous)
(W3 <- weightit(re75 ~ age + educ + married +
nodegree + re74, data = lalonde,
method = "energy", moments = 1))
#> A weightit object
#> - method: "energy" (energy balancing)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: continuous
#> - covariates: age, educ, married, nodegree, re74
summary(W3)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> all 0 |---------------------------| 9.7874
#>
#> - Units with the 5 most extreme weights:
#>
#> 501 486 180 166 171
#> all 6.7811 6.8314 7.7046 9.3441 9.7874
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> all 1.209 0.817 0.564 98
#>
#> - Effective Sample Sizes:
#>
#> Total
#> Unweighted 614.
#> Weighted 249.59
bal.tab(W3, poly = 2)
#> Balance Measures
#> Type Corr.Adj
#> age Contin. 0.0000
#> educ Contin. 0.0000
#> married Binary 0.0000
#> nodegree Binary 0.0000
#> re74 Contin. 0.0000
#> age² Contin. -0.0106
#> educ² Contin. 0.0117
#> re74² Contin. -0.0440
#>
#> Effective sample sizes
#> Total
#> Unadjusted 614.
#> Adjusted 249.59
# }