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Matching on generalized propensity scores with continuous exposures


An R package for implementing matching on generalized propensity scores with continuous exposures. We developed an innovative approach for estimating causal effects using observational data in settings with continuous exposures, and introduce a new framework for GPS caliper matching that jointly matches on both the estimated GPS and exposure levels to fully adjust for confounding bias.



Developing Docker image can be downloaded from Docker Hub. See more details in docker_singularity.


Input parameters:

Y A vector of observed outcome variable.
w A vector of observed continuous exposure variable.
c A data.frame or matrix of observed covariates variable.
ci_appr The causal inference approach. Possible values are:
- “matching”: Matching by GPS
- “weighting”: Weighting by GPS
gps_model Model type which is used for estimating GPS value, including parametric (default) and non-parametric.
use_cov_transform If TRUE, the function uses transformer to meet the covariate balance.
transformers A list of transformers. Each transformer should be a unary function. You can pass name of customized function in the quotes.
Available transformers:
- pow2: to the power of 2
- pow3: to the power of 3
bin_seq Sequence of w (treatment) to generate pseudo population. If NULL is passed the default value will be used, which is seq(min(w)+delta_n/2,max(w), by=delta_n).
trim_quantiles A numerical vector of two. Represents the trim quantile level. Both numbers should be in the range of [0,1] and in increasing order (default: c(0.01,0.99)).
params Includes list of params that is used internally. Unrelated parameters will be ignored.
sl_lib: A vector of prediction algorithms. nthread An integer value that represents the number of threads to be used by internal packages.
... Additional arguments passed to different models.

Additional parameters

Causal Inference Approach (ci.appr)

pseudo_pop <- generate_pseudo_pop(Y,
                                  ci_appr = "matching",
                                  gps_model = "parametric",
                                  use_cov_transform = TRUE,
                                  transformers = list("pow2", "pow3"),
                                  sl_lib = c("m_xgboost"),
                                  params = list(xgb_nrounds = 50,
                                                xgb_max_depth = 6,
                                                xgb_eta = 0.3,
                                                xgb_min_child_weight = 1),
                                  nthread = 1,
                                  covar_bl_method = "absolute",
                                  covar_bl_trs = 0.1,
                                  trim_quantiles = c(0.01,0.99),
                                  max_attempt = 1,
                                  matching_fun = "matching_l1",
                                  delta_n = 1,
                                  scale = 1)

matching_l1 is Manhattan distance matching approach. For prediction model we use SuperLearner package. SuperLearner supports different machine learning methods and packages. params is a list of hyperparameters that users can pass to the third party libraries in the SuperLearner package. All hyperparameters go into the params list. The prefixes are used to distinguished parameters for different libraries. The following table shows the external package names, their equivalent name that should be used in sl_lib, the prefixes that should be used for their hyperparameters in the params list, and available hyperparameters.

Package name sl_lib name prefix available hyperparameters
XGBoost m_xgboost xgb_ nrounds, eta, max_depth, min_child_weight
ranger m_ranger rgr_ num.trees, write.forest, replace, verbose, family

nthread is the number of available threads (cores). XGBoost needs OpenMP installed on the system to parallel the processing. use_covariate_transform activates transforming covariates in order to achieve covariate balance. Users can pass custom function name in a list to be included in the processing. At each iteration, which is set by the users using max_attempt, the column that provides the worst covariate balance will be transformed.

data_with_gps <- estimate_gps(Y,
                              gps_model = "parametric",
                              internal_use = FALSE,
                              params = list(xgb_nrounds = 50,
                                            xgb_max_depth = 6,
                                            xgb_eta = 0.3,
                                            xgb_min_child_weight = 1),
                              nthread = 1,                                
                              sl_lib = c("m_xgboost")

If internal_use is set to be TRUE, the program will return additional vectors to be used by the selected causal inference approach to generate a pseudo population. See ?estimate_gps for more details.

estimate_npmetric_erf <- function(matched_Y,
                                  matched_counter = NULL,
                                  bw_seq = seq(0.2,2,0.2),
syn_data <- generate_syn_data(sample_size=1000,
                              outcome_sd = 10,
                              gps_spec = 1,
                              cova_spec = 1)


For more information about reporting bugs and contribution, please read the contribution page from the package web page.


  1. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.