Flags units whose (treatment, covariate) configuration is poorly covered
by the fitted joint – the proxy's mass coverage is the positivity
diagnostic. For each treatment arm the squared Mahalanobis distance to the
nearest regime centre is converted to an upper-tail chi-square coverage
probability; the reported coverage is the minimum across arms, since the
treatment effect needs both arms supported. Units below floor are flagged
and excluded from proxy_policy_value() by default.
Arguments
- model
An uplift_model.
- newdata
A data frame carrying the covariate columns.
- t1, t0
The treated and control treatment values. Default
1and0.- floor
Coverage probability below which a unit is flagged. Default
0.01.
Value
A data.table::data.table with columns id, coverage,
overlap_flag.
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_policy_value(),
proxy_predict(),
proxy_regime_segments(),
proxy_retrospective_uplift(),
proxy_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_overlap(m, newdata = data.frame(x = c(0, 8)))
#> id coverage overlap_flag
#> <int> <num> <lgcl>
#> 1: 1 5.887126e-01 FALSE
#> 2: 2 4.893549e-13 TRUE