Y A vector of observed outcome
w A vector of observed continuous exposure
c A data.frame or matrix of observed
ci_appr The causal inference approach.
Possible values are:
- “matching”: Matching by GPS
- “weighting”: Weighting by GPS
gps_density Model density type which is
used for estimating GPS value, including normal (default) and
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.
- 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
exposure_trim_qtls A numerical vector of
two. Represents the trim quantile level for exposure value. Both numbers
should be in the range of [0,1] and in increasing order (default:
gps_trim_qtls A numerical
vector of two. Represents the trim quantile level for gps value. Both
numbers should be in the range of [0,1] and in increasing order
(default: c(0.0, 1.0)).
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
set.seed(422) n <- 10000 mydata <- generate_syn_data(sample_size = n) year <- sample(x=c("2001", "2002", "2003", "2004", "2005"), size = n, replace = TRUE) region <- sample(x=c("North", "South", "East", "West"),size = n, replace = TRUE) mydata$year <- as.factor(year) mydata$region <- as.factor(region) mydata$cf5 <- as.factor(mydata$cf5) pseudo_pop <- generate_pseudo_pop( mydata[, c("id", "Y")], mydata[, c("id", "w")], mydata[, c("id", "cf1", "cf2", "cf3", "cf4", "cf5", "cf6","year","region")], ci_appr = "matching", gps_density = "kernel", use_cov_transform = TRUE, transformers = list("pow2", "pow3", "abs", "scale"), exposure_trim_qtls = c(0.01,0.99), sl_lib = c("m_xgboost"), covar_bl_method = "absolute", covar_bl_trs = 0.1, covar_bl_trs_type = "mean", max_attempt = 4, dist_measure = "l1", delta_n = 1, scale = 0.5, nthread = 1) plot(pseudo_pop)
matching_fn is Manhattan distance
matching approach. For prediction model we use SuperLearner
package. SuperLearner supports different machine learning methods and
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
prefixes that should be used for their hyperparameters in the
params list, and available
||nrounds, eta, max_depth, min_child_weight|
||num.trees, write.forest, replace, verbose, family|
nthread is the number of available
threads (cores). XGBoost needs OpenMP installed on the system to
parallelize the processing.
The CausalGPS package is logging internal activities into the
CausalGPS.log file. The file is located in the source file
location and will be appended. Users can change the logging file name
(and path) and logging threshold. The logging mechanism has different
thresholds (see logger package).
The two most important thresholds are INFO and DEBUG levels. The former,
which is the default level, logs more general information about the
process. The latter, if activated, logs more detailed information that
can be used for debugging purposes.