Diagnose positivity with effective data points

check_edp() computes the effective-data-points (EDP) positivity diagnostic of Ring and Schomaker (2026) for binary, categorical, and continuous exposures. For every observation moved to a candidate intervention value, it sums a product kernel over the sample to measure how much observed support surrounds that intervened-on point.

Usage

check_edp(
  .data,
  .exposure,
  .covariates,
  .outcome_covariates = NULL,
  .treatment_covariates = NULL,
  values = NULL,
  variant = c("data", "estimator"),
  kernel = c("gaussian"),
  bw_exposure = NULL,
  bw_covariates = NULL,
  categorical_similarity = 0,
  exposure_type = c("auto", "binary", "categorical", "continuous")
)

Arguments

.data: A data frame.
.exposure: The exposure column, selected with data-masking.
.covariates: The covariate columns, selected with tidyselect.
.outcome_covariates: The outcome-model covariate columns for the estimator variant, selected with tidyselect. Defaults to .covariates.
.treatment_covariates: The treatment-model covariate columns for the estimator variant, selected with tidyselect. Defaults to .covariates.
values: The intervention values $a^*$. NULL (the default) uses every level of a factor exposure and every observed value of a binary or character exposure, and the deciles of the observed exposure for continuous exposures.
variant: One of "data" (report a single edp per point, the default) or "estimator" (report edp_outcome, edp_treatment, and ideal_weight).
kernel: The continuous-dimension kernel. Only "gaussian" is currently available; categorical dimensions always use the match kernel.
bw_exposure: The continuous-exposure half-distance. NULL (the default) uses 0.5 * sd(exposure). A half-distance of 0 counts exact matches.
bw_covariates: The numeric-covariate half-distance, applied to every numeric covariate. NULL (the default) uses 1 * sd per covariate.
categorical_similarity: The kernel value in [0, 1] for non-matching categories, shared by categorical exposures and factor or character covariates. Defaults to 0. A positive value masks categorical violations.
exposure_type: One of "auto" (detect from the data, the default), "binary", "categorical", or "continuous". A supplied type is authoritative and detection is not consulted, so it is rejected only when the exposure column cannot carry it: "continuous" needs a numeric column and "binary" needs exactly two distinct values, while "categorical" asks nothing of the column.

Value

An edp_result object, an S7 subclass of positivity_diagnostic. Its @results tibble has one row per observation and intervention value. The data variant carries columns .id (the observation row index), value (the intervention value $a^*$), and edp. The estimator variant replaces edp with edp_outcome, edp_treatment, and ideal_weight. It also carries the @variant and @bandwidths properties.

generics::glance() returns a one-row tibble with n (the sample size), variant, n_values (the number of intervention values), and the observed range of every measure the variant reports. That is edp_min and edp_max for the data variant, and edp_outcome_min, edp_outcome_max, edp_treatment_min, edp_treatment_max, ideal_weight_min, and ideal_weight_max for the estimator variant.

Details

For each observation $i$ the diagnostic forms the intervened-on point $o^*_i = (l_i, a^*)$, holding the covariates at their observed values and setting the exposure to an intervention value $a^*$. The effective data points at that point are $$\mathrm{EDP}_i(a^*) = \sum_{j=1}^{n} k(o_j, o^*_i),$$ a sum over the whole sample of a product kernel that multiplies one factor per dimension. Each factor lies in $[0, 1]$, so $0 \le \mathrm{EDP} \le n$. A continuous dimension with half-distance $h$ contributes $0.5^{(\delta / h)^2}$, which equals 1 at $\delta = 0$ and exactly 0.5 at $|\delta| = h$; this is a reparameterized Gaussian kernel. A categorical dimension contributes 1 on a match and categorical_similarity otherwise. Numeric covariates and continuous exposures are continuous dimensions; factor and character covariates, together with binary and categorical exposures, are categorical dimensions.

The half-distances follow the paper's rule of thumb: 1 * sd for each numeric covariate and 0.5 * sd for a continuous exposure. These are marginal scales, so a mid-support gap in a strongly multimodal exposure can be masked by an inflated standard deviation; supply bw_exposure or bw_covariates directly when a robust scale is preferable. As the half-distances grow without bound every kernel factor approaches 1 and EDP approaches n, which renders the diagnostic uninformative; at a half-distance of 0 a continuous dimension counts only exact matches.

EDP is a product kernel, so adding dimensions can only lower it. It is meaningful relative across observations and strata for a fixed covariate set, never against a universal threshold; compare a stratum against another stratum or a candidate value against another candidate value rather than against a fixed cutoff.

The estimator variant reframes EDP around the two nuisance models of a causal estimator. edp_outcome conditions on the exposure and the outcome-model covariates $(A, L)$; edp_treatment conditions on the treatment-model covariates $(L)$ alone. Because the outcome measure carries the extra exposure dimension, edp_outcome <= edp_treatment whenever the two covariate sets are equal. The reported ideal_weight is edp_treatment / edp_outcome, proportional to the inverse of the estimated treatment density at the intervention, so it grows where treatment support is thin. Where a target has no outcome-model support, edp_outcome is zero and ideal_weight is infinite, the honest value of one over an estimated density of zero.

References

Ring C, Schomaker M (2026). A Diagnostic to Find and Help Combat Stochastic Positivity Issues, with a Focus on Continuous Treatments.

Examples

set.seed(1)
n <- 100
x1 <- rnorm(n)
dose <- rnorm(n, mean = x1)
df <- data.frame(dose = dose, x1 = x1)

result <- check_edp(df, dose, x1, values = c(0, 1), exposure_type = "continuous")
result
#> 
#> ── Effective data points ───────────────────────────────────────────────────────
#> Exposure: "dose" (continuous)
#> Observations: 100
#> Variant: data
#> Intervention values: 2
#> edp range: 0.793 to 26.809