README.md
In CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures

CausalGPS

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Matching on generalized propensity scores with continuous exposures

CausalGPS is an R package that implements matching on generalized propensity scores with continuous exposures. The package introduces a novel approach for estimating causal effects using observational data in settings with continuous exposures, and a new framework for GPS caliper matching that jointly matches on both the estimated GPS and exposure levels to fully adjust for confounding bias.

Installing from source

library("devtools")
install_github("NSAPH-Software/CausalGPS")
library("CausalGPS")

Installing from CRAN

install.packages("CausalGPS")

Setting up docker environment

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

Input parameters:

w A data.frame comprised of two columns: one contains the observed exposure variable, and the other is labeled as 'id'. The column for the outcome variable can be assigned any name as per your requirements. c A data.frame of includes observed covariate variables. It should also consist of a column named 'id'. ci_appr The causal inference approach. Possible values are: - "matching": Matching by GPS - "weighting": Weighting by GPS gps_density Model type which is used for estimating GPS value, including normal (default) and kernel. 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). exposure_trim_qtls 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)). gps_trim_qtls 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.

Causal Inference Approach (`ci.appr`)

if ci.appr = 'matching':
dist_measure: Distance measuring function. Available options:
- l1: Manhattan distance matching
delta_n: caliper parameter.
scale: a specified scale parameter to control the relative weight that is attributed to the distance measures of the exposure versus the GPS.
covar_bl_method: covariate balance method. Available options:
- 'absolute'
covar_bl_trs: covariate balance threshold
covar_bl_trs_type: covariate balance type (mean, median, maximal)
max_attempt: maximum number of attempt to satisfy covariate balance.
See [create_matching()] for more details about the parameters and default values.
if ci.appr = 'weighting':
covar_bl_method: Covariate balance method.
covar_bl_trs: Covariate balance threshold
max_attempt: Maximum number of attempt to satisfy covariate balance.
Generating Pseudo Population

pseudo_pop <- generate_pseudo_pop(w,
                                  c,
                                  ci_appr = "matching",
                                  gps_density = "normal",
                                  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,
                                  exposure_trim_qtls = c(0.01,0.99),
                                  max_attempt = 1,
                                  dist_measure = "l1",
                                  delta_n = 1,
                                  scale = 1)

matching_fn 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.

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.

Estimating GPS

data_with_gps <- estimate_gps(w,
                              c,
                              gps_density = "normal",
                              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")
                              )

Generating Pseudo Population with an available GPS object

gps_obj <- estimate_gps(w,
                        c,
                        gps_density = "normal",
                        params = list(xgb_max_depth = c(3,4,5),
                                      xgb_nrounds=c(10,20,30,40,50,60)),
                        nthread = 1,
                        sl_lib = c("m_xgboost")
                        )

pseudo_pop <- generate_pseudo_pop(w,
                                  c,
                                  ci_appr = "matching",
                                  gps_obj = gps_obj,
                                  use_cov_transform = TRUE,
                                  trim_quantiles = c(0.01,0.99),
                                  covar_bl_method = "absolute",
                                  covar_bl_trs = 0.1,
                                  covar_bl_trs_type = "mean",
                                  dist_measure = "l1",
                                  max_attempt = 1,
                                  delta_n = 1,
                                  scale = 0.5,
                                  nthread = 12)

Estimating Exposure Rate Function

estimate_npmetric_erf <- function(matched_Y,
                                  matched_w,
                                  matched_counter = NULL,
                                  bw_seq = seq(0.2,2,0.2),
                                  w_vals,
                                  nthread)

Generating Synthetic Data

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.

Please note that the CausalGPS project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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.