For a binary treatment, the uplift is exactly the conditional average
treatment effect. This is a thin alias of proxy_cate() kept for the
next-best-action vocabulary.
Arguments
- model
An uplift_model.
- newdata
A data frame carrying the covariate columns.
- ...
Forwarded to
fit_proxymix()inside the"mc"refits.
Value
A data.table::data.table – see proxy_cate().
See also
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_retrospective_uplift(),
uplift_identification(),
uplift_model()
Examples
set.seed(1)
dat <- data.frame(y = stats::rnorm(200), t = stats::rbinom(200, 1L, 0.5),
x = stats::rnorm(200))
m <- fit_uplift(dat, "y", "t", "x", N = 1L, regime = "moment")
proxy_uplift(m, newdata = data.frame(x = 0))
#> id tau se ci_lo ci_hi overlap_flag
#> <int> <num> <num> <num> <num> <lgcl>
#> 1: 1 0.02079754 0.1311432 -0.2362383 0.2778334 FALSE