generate_pseudo_pop {CausalGPS}

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")