With method = "gbm", added the ability to tune hyperparameters like interaction.depth and distribution using the same critera as is used to select the optimal tree. A summary of the tuning results is included in info in the weightit output object.
Fixed a bug where moments and int were ignored unless both were specified.
Effective sample sizes now print only up to two digits (believe me, you don’t need three) and print more cleanly with whole numbers.
Fixed a bug when using by, thanks to @frankpopham. (#11)
Fixed a bug when using weightitMSM with methods that process int and moments (though you probably shouldn’t use them anyway). Thanks to Sven Reiger.
Fixed a bug when using method = "npcbps" where weights could be excessively small and mistaken for all being them. The weights now sum to the number of units.
Added support for energy balancing with method = "energy". This method minimizes the energy distance between samples, which is a multivariate distance measure. This method uses code written specifically for WeightIt (i.e., it does not call a package specifically designed for energy balancing) using the osqp package for the optimization (same as optweight). See Huling & Mak (2020) for details on this method. Also included is an option to require exact balance on moments of the covariates while minimizing the energy distance. The method works for binary and multinomial treatments with the ATE, ATT, or ATC. Sampling weights are supported. Because the method requires the calculation and manipulation of a distance matrix for all units, it can be slow and/or memory intensive for large datasets.
Improvements to method = "gbm" and to method = "super" with SL.method = "method.balance". A new suite of stop.methods are allowed. For binary treatments, these include the energy distance, sample Mahalanobis distance, and pseudo-R2 of the weighted treatment model, among others. See ?stop.method for allowable options. In addition, performance for both is quite a bit faster.
With multinomial treatments with link = "logit" (the default), if the mnlogit package is installed, it can be requested for estimating the propensity score by setting the option use.mnlogit = TRUE. It should give the same results as the default, which uses mlogit, but can be faster for large datasets.
Added option estimand = "ATOS" for the “optimal subset” treatment effect as described by Crump et al. (2009). This estimand finds the subset of units who, with ATE weights applied, yields a treatment effect with the lowest variance, assuming homoscedasticity (and other assumptions). It is only available for binary treatments with method = "ps". In general it makes more sense to use estimand = "ATO" if you want a low-variance estimate and don’t care about the target population, but I added this here for completeness. It is available in get_w_from_ps() as well.
make_full_rank() is now faster.
Cleaning up of some error messages.
Fixed a bug when using link = "log" for method = "ps" with binary treatments.
Fixed a bug when using method = "cbps" with continuous treatments and sampling weights. Previously the returned weights included the sampling weights multiplied in; now they are separated, as they are in all other scenarios and for all other methods.
Improved processing of non-0/1 binary treatments, including for method = "gbm". A guess will be made as to which treatment is considered “treated”; this only affects produced propensity scores but not weights.
Changed default value of at in trim() from .99 to 0.
Added output for the number of weights equal to zero in summary.weightit. This can be especially helpful when using "optweight" or "energy" methods or when using estimand = "ATOS".
Added support for entropy balancing (method = "ebal") for continuous treatments as described by Tübbicke (2020). Relies on hand-written code contributed by Stefan Tübbicke rather than another R package. Sampling weights and base weights are both supported as they are with binary and multi-category treatments.
Added support for Balance SuperLearner as described by Pirracchio and Carone (2018) with method = "super". Rather than using NNLS to choose the optimal combination of predictions, you can now optimize balance. To do so, set SL.method = "method.balance". You will need to set an argument to stop.method, which works identically to how it does for method = "gbm". For example, for stop.method = "es.max", the predicted values given will be the combination of predicted values that minimizes the largest absolute standardized mean difference of the covariates in the sample weighted using the predicted values as propensity scores.
Changed some of the statistics displayed when using summary(): the weight ratio is gone (because weights can be 0, which is not problematic but would explode the ratio), and the mean absolute deviation and entropy of the weights are now present.
Added crayon for prettier printing of summary() output.
Formula interfaces now accept poly(x, .) and other matrix-generating functions of variables, including the rms-class-generating functions from the rms package (e.g., pol(), rcs(), etc.) (the rms package must be loaded to use these latter ones) and the basis-class-generating functions from the splines package (i.e., bs() and ns()). A bug in an early version of this was found by @ahinton-mmc.
Added support for marginal mean weighting through stratification (MMWS) as described by Hong (2010, 2012) for weightit() and get_w_from_ps() through the subclass argument (see References at ?get_w_from_ps). With this method, subclasses are formed based on the propensity score and weights are computed based on the number of units in each subclass. MMWS can be used with any method that produces a propensity score. The implementation here ensures all subclasses have a least one member by filling in empty subclasses with neighboring units.
Added stabilize option to get_w_from_ps().
A new missing argument has been added to weightit() to choose how missing data in the covariates is handled. For most methods, only "ind" (i.e., missing indicators with single-value imputation) is allowed, but for "ps", "gbm", and "twang", other methods are possible. For method = "ps", a stochastic approximation of the EM algorithm (SAEM) can be used through the misaem package by setting missing = "saem".
For continuous treatments with the "ps", "gbm", and "super" methods (i.e., where the conditional density of the treatment needs to be estimated), the user can now supply their own density as a string or function rather than using the normal density or kernel density estimation. For example, to use the density of the t-distribution with 3 degrees of freedom, one can set density = "dt_3". T-distributions often work better than normal distributions for extreme values of the treatment.
Some methods now have an info component in the output object. This contains information that might be useful in diagnosing or reporting the method. For example, when method = "gbm", info contains the tree that was used to compute the weights and the balance resulting from all the trees, which can be plotted using plot(). When method = "super", info contains the coefficients in the stacking model and the cross-validation risk of each of the component methods.
For method = "gbm", the best tree can be chosen using cross validation rather than balance by setting stop.method = "cv5", e.g., to do 5-fold cross-validation.
For method = "gbm", a new optional argument start.tree can be set to select the tree at which balance begins to be computed. This can speed things up when you know that the best tree is not within the first 100 trees, for example.
When using method = "gbm" with multi-category treatments and estimands other than the ATE, ATT, or ATC are used with standardized mean differences as the stopping rule, the mean differences will be between the weighted overall sample and each treatment group. Otherwise, some efficiency improvements.
When using method = "ps" with multi-category treatments, the use of use.mlogit = FALSE to request multiple binary regressions instead of multinomial regression is now documented and an associated bug is now fixed, thanks to @ahinton-mmc.
When use method = "super", one can now set discrete = TRUE to use discrete SuperLearner instead of stacked SuperLearner, but you probably shouldn’t.
moments and int can now be used with method = "npcbps".
Performance enhancements.
Fixed bug when using weightit() inside another function that passed a by argument explicitly. Also changed the syntax for by; it must now either be a string (which was always possible) or a one-sided formula with the stratifying variable on the right-hand side. To use a variable that is not in data, you must use the formula interface.
Fixed bug when trying to use ps with by in weightit().
Added new sbps() function for estimating subgroup balancing propensity score weights, including both the standard method and a new smooth version.
Setting method = "gbm" and method = "twang" will now do two different things. method = "gbm" uses gbm and cobalt functions to estimate the weights and is much faster, while method = "twang" uses twang functions to estimate the weights. The results are similar between the two methods. Prior to this version, method = "gbm" and method = "twang" both did what method = "twang" does now.
Bug fixes when stabilize = TRUE, thanks to @ulriksartipy and Sven Rieger.
Fixes for using base.weight argument with method = "ebal". Now the supplied vector should have a length equal to the number of units in the dataset (in contrast to its use in ebalance, which requires a length equal to the number of control units).
Restored dependency on cobalt for examples and vignette.
When method = "ps" and the treatment is ordered (i.e., ordinal), MASS::polr() is used to fit an ordinal regression. Make the treatment un-ordered to to use multinomial regression instead.
Added support for using bias-reduced fitting functions when method = "ps" as provided by the brglm2 package. These can be accessed by changing the link to, for example, "br.logit" or "br.probit". For multinomial treatments, setting link = "br.logit" fits a bias-reduced multinomial regression model using brglm2::brmultinom(). This can be helpful when regular maximum likelihood models fail to converge, though this may also be a sign of lack of overlap.
Bug fixes. Functions now work better when used inside other functions (e.g., lapply).
Behavior of weightit() in the presence of non-NULL focal has changed. When focal is specified, estimand is assumed to be ATT. Previously, focal would be ignored unless estimand = "ATT".
Processing of estimand and focal is improved. Functions are smarter about guessing which group is the focal group when one isn’t specified, especially with non-numeric treatments. focal can now be used with estimand = "ATC" to indicate which group is the control group, so "ATC" and "ATT" now function more similarly.
Added function get_w_from_ps() to transform propensity scores into weights (instead of having to go through weightit()).
Added functions as.weightit() and as.weightitMSM() to convert weights and treatments and other components into weightit objects so that summary.weightit() can be used on them.
Updated documentation to describe how missing data in the covariates is handled. Some bugs related to missing data have been fixed as well, thanks to Yong Hao Pua.
ps.cont() had the “z-transformed correlation” options removed to simplify output. This function and its supporting functions will be deprecated as soon as the new version of twang is released.
When using method = "ps" or method = "super" with continuous treatments, setting use.kernel = TRUE and plot = TRUE, the plot is now made with ggplot2 rather than the base R plots.
Added plot.summary.weightit() to plot the distribution of weights (a feature also in optweight).
Removed dependency on cobalt temporarily, which means the examples and vignette won’t run.
Added ggplot2 to Imports.
Fixed a bug when using the ps argument in weightit().
Fixed a bug when setting include.obj = TRUE in weightitMSM().
Added warnings for using certain methods with longitudinal treatments as they are not validated and may lead to incorrect inferences.
Added super method to estimate propensity scores using the SuperLearner package.
Added optweight method to estimate weights using optimization (but you should probably just use the optweight package).
weightit() now uses the correct formula to estimate weights for the ATO with multinomial treatments as described by Li & Li (2018).
Added include.obj option in weightit() and weightitMSM() to include the fitted object in the output object for inspection. For example, with method = "ps", the glm object containing the propensity score model will be included in the output.
Rearranged the help pages. Each method now has its own documentation page, linked from the weightit help page.
Propensity scores are now included in the output for binary treatments with gbm and cbps methods. Thanks to @Blanch-Font for the suggestion.
Other bug fixes and minor changes.
Added trim() function to trim weights.
Added ps.cont() function, which estimates generalized propensity score weights for continuous treatments using generalized boosted modeling, as in twang. This function uses the same syntax as ps() in twang, and can also be accessed using weightit() with method = "gbm". Support functions were added to make it compatible with twang functions for assessing balance (e.g., summary, bal.table, plot). Thanks to Donna Coffman for enlightening me about this method and providing the code to implement it.
The input formula is now much more forgiving, allowing objects in the environment to be included. The data argument to weightit() is now optional. To simplify things, the output object no longer contains a data field.
Under-the-hood changes to facilitate adding new features and debugging. Some aspects of the output objects have been slightly changed, but it shouldn’t affect use for most users.
Fixed a bug where variables would be thrown out when method = "ebal".
Added new moments and int options for some weightit() methods to easily specify moments and interactions of covariates.
Fixed bug when using objects not in the data set in weightit(). Behavior has changed to include transformed covariates entered in formula in weightit() output.
Fixed bug resulting from potentially colinearity when using ebal or ebcw.
Added a vignette.
Edits to code and help files to protect against missing CBPS package.
Corrected sampling weights functionality so they work correctly. Also expanded sampling weights to be able to be used with all methods, including those that do not natively allow for sampling weights (e.g., ATE).
Minor bug fixes and spelling corrections.
Added weightitMSM() function (and supporting print() and summary() functions) to estimate weights for marginal structural models with time-varying treatments and covariates.
Fixed some bugs, including when using CBPS with continuous treatments, and when using focal incorrectly.
Added method = "sbw" for stable balancing weights (now removed and replaced with method = "optweight")
Allowed for estimation of multinomial propensity scores using multiple binary regressions if mlogit is not installed
Allowed for estimation of multinomial CBPS using multiple binary CBPS for more than 4 groups
Added README and NEWS