Predict from a Fitted Density-Ratio Model

Applies a density_ratio_fit object (from fit_density_ratio()) to new (x, z) rows. All four backends compute ratios in log-space internally for numerical stability; type controls the returned representation.

Usage

predict_density_ratio(
  object,
  new_x,
  new_z,
  type = c("log_ratio", "weight", "ratio")
)

Arguments

object

A density_ratio_fit.

new_x

Numeric vector or matrix. Treatment values to evaluate.

new_z

Numeric matrix or data.frame. Confounders to evaluate.

type

Character. Return type: "log_ratio" – natural-log density ratio (default; preferred for downstream calculation); "ratio" – raw density ratio (exp(log_ratio)); "weight" – IES-compatible normalised weights (positive, sum-to-n_new).

Value

Numeric vector of length nrow(new_x).

See also

fit_density_ratio().

Other density ratio and propensity: assess_overlap(), effective_sample_size(), estimate_density_ratio(), estimate_propensity(), fit_density_ratio(), plot_weights()

Examples

set.seed(1L)
n <- 200L
z <- matrix(rnorm(n * 2L), n, 2L)
x <- z[, 1L] + rnorm(n)
fit <- fit_density_ratio(x, z, method = "logistic", seed = 1L)
weights <- predict_density_ratio(fit, new_x = x, new_z = z,
                                 type = "weight")
summary(weights)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.8512  0.9721  1.0000  1.0000  1.0270  1.1256