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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.
library("devtools")
install_github("fasrc/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 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
pred_model a prediction model (use “sl”
for SuperLearner)
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)).
optimized_compile If TRUE, uses counts to
keep track of number of replicated pseudo population.
params Includes list of params that is
used internally. Unrelated parameters will be ignored.
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",
pred_model = "sl",
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),
optimized_compile = TRUE,
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. User need to pass sl as pred_model to
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,
w,
c,
pred_model = "sl",
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_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.