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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.
library("devtools")
install_github("NSAPH-Software/CausalGPS")
library("CausalGPS")install.packages("CausalGPS")Developing Docker image can be downloaded from Docker Hub. See more details in docker_singularity.
Input parameters:
Y A data.frame comprised of two
columns: one contains the observed outcome variable, and the other is
labeled as ‘id’. The column for the outcome variable can be assigned any
name as per your requirements.
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.
ci.appr)pseudo_pop <- generate_pseudo_pop(Y,
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.
| 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(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")
)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(Y,
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)estimate_npmetric_erf <- function(matched_Y,
matched_w,
matched_counter = NULL,
bw_seq = seq(0.2,2,0.2),
w_vals,
nthread)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.