estimate_gps: Estimate generalized propensity score (GPS) values

In CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures

Estimate generalized propensity score (GPS) values

Description

Estimates GPS value for each observation using normal or kernel approaches.

Usage

estimate_gps(
  .data,
  .formula,
  gps_density = "normal",
  sl_lib = c("SL.xgboost"),
  ...
)

Arguments

.data	A data frame of observed continuous exposure variable and observed covariates variable. Also includes id column for future references.
.formula	A formula specifying the relationship between the exposure variable and the covariates. For example, w ~ I(cf1^2) + cf2.
gps_density	Model type which is used for estimating GPS value, including normal (default) and kernel.
sl_lib	A vector of prediction algorithms to be used by the SuperLearner packageg.
...	Additional arguments passed to the model.

Value

The function returns a S3 object. Including the following:

• .data : id, exposure_var, gps, e_gps_pred, e_gps_std_pred, w_resid

• params: Including the following fields:

  • gps_mx (min and max of gps)

  • w_mx (min and max of w).

  • .formula

  • gps_density

  • sl_lib

  • fcall (function call)

Examples

m_d <- generate_syn_data(sample_size = 100)
data_with_gps <- estimate_gps(.data = m_d,
                              .formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 + cf6,
                              gps_density = "normal",
                              sl_lib = c("SL.xgboost")
                             )

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