Retrospective (counterfactual-mean) uplift for observed units

For each observed unit (y, t, x), the counterfactual-mean uplift of moving from t0 to t1, \(E[Y_{t_1} \mid y, t, x] - E[Y_{t_0} \mid y, t, x]\), computed by gmm_counterfactual(). Unlike proxy_cate(), the abduction gate uses the observed outcome y as well, sharpening the per-unit estimate. Only the counterfactual mean is identified; the spread is not (see gmm_cf_variance()).

Usage

proxy_retrospective_uplift(model, observed, t1 = 1, t0 = 0)

Arguments

model

An uplift_model.

observed

A data frame carrying the outcome, treatment, and covariate columns of the observed units.

t1, t0

The treated and control treatment values. Default 1 and 0.

Value

A data.table::data.table with columns id, y_obs, t_obs, cf_mean_t1, retro_uplift.

See also

gmm_counterfactual(), proxy_cate()

Other decision: fit_uplift(), gmm_cf_mean(), gmm_cf_tail_prob(), gmm_cf_variance(), gmm_counterfactual(), gmm_intervene(), proxy_cate(), proxy_confounding_gap(), proxy_decide(), proxy_identification_report(), proxy_overlap(), proxy_policy_value(), proxy_predict(), proxy_regime_segments(), proxy_uplift(), uplift_identification(), uplift_model()

Examples

set.seed(1)
n <- 400L
x <- stats::rnorm(n)
t <- stats::rbinom(n, 1L, 0.5)
y <- 1 + (0.5 + x) * t + stats::rnorm(n, sd = 0.5)
dat <- data.frame(y = y, t = t, x = x)
m <- fit_uplift(dat, "y", "t", "x", N = 2L, regime = "sample",
                max_iter = 80L, seed = 1L)
proxy_retrospective_uplift(m, observed = dat[1:5, ])
#>       id     y_obs t_obs cf_mean_t1 retro_uplift
#>    <int>     <num> <num>      <num>        <num>
#> 1:     1 0.7030127     1  0.7030127            0
#> 2:     2 1.7512123     0  1.7512123            0
#> 3:     3 0.9285252     1  0.9285252            0
#> 4:     4 3.3663765     1  3.3663765            0
#> 5:     5 0.9316633     0  0.9316633            0