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Confounding gap: the sensitivity of the effect to the latent regime

Source: R/identification.R
proxy_confounding_gap.Rd

Per-unit difference between the ignorability-mode and do-mode effects, \(\Delta(x) = \tau_{\mathrm{obs}}(x) - \tau_{\mathrm{do}}(x)\). Under ignorability the two coincide and \(\Delta \equiv 0\); a non-zero gap is a sensitivity signal – how much the estimated effect would move if a fitted regime confounded treatment and outcome beyond X – not a correction the data licenses.

Usage

proxy_confounding_gap(model, newdata, t1 = NULL, t0 = NULL)

Arguments

model

An uplift_model.

newdata

A data frame carrying the covariate columns.

t1, t0

The treated and control treatment values. Default the treatment levels observed at fit time (model@treatment_levels); a value matching neither observed level aborts.

Value

A data.table::data.table with columns id, tau_obs, tau_do, gap, overlap_flag.

Details

When each regime holds a single treatment arm, as often happens with a binary treatment, the within-regime effect is zero, the gap equals the whole estimated effect, and a warning of class proxymix_constant_treatment is raised.

See also

proxy_cate(), proxy_identification_report()

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

Examples

set.seed(1)
n <- 600L
u <- stats::rbinom(n, 1L, 0.5) # unobserved group
x <- stats::rnorm(n)
t <- stats::rbinom(n, 1L, 0.3 + 0.4 * u)
y <- 1 + 5 * u + 0.5 * t + x + 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_confounding_gap(m, data.frame(x = c(-1, 0, 1)))
#>       id  tau_obs    tau_do      gap overlap_flag
#>    <int>    <num>     <num>    <num>       <lgcl>
#> 1:     1 2.908393 0.5072191 2.401174        FALSE
#> 2:     2 2.868516 0.5105320 2.357984        FALSE
#> 3:     3 2.801447 0.5132839 2.288164        FALSE