generate_pseudo_pop: Generate pseudo population

In CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures

Generate pseudo population

Description

Generates pseudo population data set based on user-defined causal inference approach. The function uses an adaptive approach to satisfies covariate balance requirements. The function terminates either by satisfying covariate balance or completing the requested number of iteration, whichever comes first.

Usage

generate_pseudo_pop(
  .data,
  cw_obj,
  covariate_col_names,
  covar_bl_trs = 0.1,
  covar_bl_trs_type = "maximal",
  covar_bl_method = "absolute"
)

Arguments

.data	A data.frame of observation data with id column.
cw_obj	An S3 object of counter_weight.
covariate_col_names	A list of covariate columns.
covar_bl_trs	Covariate balance threshold
covar_bl_trs_type	Type of the covariance balance threshold.
covar_bl_method	Covariate balance method.

Value

Returns a pseudo population (gpsm_pspop) object that is generated or augmented based on the selected causal inference approach (ci_appr). The object includes the following objects:

• params

  • ci_appr

  • params

• pseudo_pop

• adjusted_corr_results

• original_corr_results

• best_gps_used_params

• effect size of generated pseudo population

Examples

set.seed(967)

m_d <- generate_syn_data(sample_size = 200)
m_d$id <- seq_along(1:nrow(m_d))

m_xgboost <- function(nthread = 4,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps_1 <- estimate_gps(
  .data = m_d,
  .formula = w ~ I(cf1^2) + cf2 + I(cf3^2) + cf4 + cf5 + cf6,
  sl_lib = c("m_xgboost"),
  gps_density = "normal")

cw_object_matching <- compute_counter_weight(gps_obj = data_with_gps_1,
                                             ci_appr = "matching",
                                             bin_seq = NULL,
                                             nthread = 1,
                                             delta_n = 0.1,
                                             dist_measure = "l1",
                                             scale = 0.5)

pseudo_pop <- generate_pseudo_pop(.data = m_d,
                                  cw_obj = cw_object_matching,
                                  covariate_col_names = c("cf1", "cf2",
                                                          "cf3", "cf4",
                                                          "cf5", "cf6"),
                                  covar_bl_trs = 0.1,
                                  covar_bl_trs_type = "maximal",
                                  covar_bl_method = "absolute")

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