compile_pseudo_pop: Compile pseudo population

In CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures

Compile pseudo population

Description

Compiles pseudo population based on the original population and estimated GPS value.

Usage

compile_pseudo_pop(
  data_obj,
  ci_appr,
  gps_density,
  exposure_col_name,
  nthread,
  ...
)

Arguments

data_obj	A S3 object including the following: Original data set + GPS values e_gps_pred e_gps_std_pred w_resid gps_mx (min and max of gps) w_mx (min and max of w).
ci_appr	Causal inference approach.
gps_density	Model type which is used for estimating GPS value, including normal and kernel.
exposure_col_name	Exposure data column name.
nthread	An integer value that represents the number of threads to be used by internal packages.
...	Additional parameters.

Details

For matching approach, use an extra parameter, bin_seq, which is 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).

Value

compile_pseudo_pop returns the pseudo population data that is compiled based on the selected causal inference approach.

Examples

set.seed(112)
m_d <- generate_syn_data(sample_size = 100)

m_xgboost <- function(nthread = 1,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps <- estimate_gps(.data = m_d,
                              .formula = w ~ cf1 + cf2 + cf3 +
                                             cf4 + cf5 + cf6,
                              gps_density = "normal",
                              sl_lib = c("m_xgboost")
                             )


pd <- compile_pseudo_pop(data_obj = data_with_gps,
                         ci_appr = "matching",
                         gps_density = "normal",
                         bin_seq = NULL,
                         exposure_col_name = c("w"),
                         nthread = 1,
                         dist_measure = "l1",
                         covar_bl_method = 'absolute',
                         covar_bl_trs = 0.1,
                         covar_bl_trs_type= "mean",
                         delta_n = 0.5,
                         scale = 1)

CausalGPS documentation built on June 22, 2024, 9:31 a.m.