compile_pseudo_pop: Compile Pseudo Population

In CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures

Description

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

Usage

compile_pseudo_pop(
  dataset,
  ci_appr,
  gps_model = "parametric",
  bin_seq = NULL,
  nthread = 1,
  trim_quantiles,
  optimized_compile,
  ...
)

Arguments

dataset	List of size 6 including the following: Original data set + GPS values (Y, w, GPS, counter, row_index, c) 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_model	Model type which is used for estimating GPS value, including parametric and non-parametric.
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).
nthread	An integer value that represents the number of threads to be used by internal packages.
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.
...	Additional parameters.

Value

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

Note

The input data set should be output of estimate_gps function with internal_use flag activated.

Examples

m_d <- generate_syn_data(sample_size = 100)
data_with_gps <- estimate_gps(m_d$Y,
                              m_d$treat,
                              m_d[c("cf1","cf2","cf3","cf4","cf5","cf6")],
                              pred_model = "sl",
                              gps_model = "parametric",
                              internal_use = TRUE,
                              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")
                             )


pd <- compile_pseudo_pop(dataset = data_with_gps,
                         ci_appr = "matching",
                         gps_model = "parametric",
                         bin_seq = NULL,
                         nthread = 1,
                         trim_quantiles = c(0.01, 0.99),
                         optimized_compile=TRUE,
                         matching_fun = "matching_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 Feb. 7, 2022, 5:12 p.m.