Run IPW / AIPW / IRM-DML on the three canonical (T, Y) pairs.

Returns one row per (pair, estimator) combination with the ATE, SE, 95% CI, and per-row notes. The IRM-DML row uses the ATE component of morie_otis_irm_dml()'s output (not the ATTE / ATC). Concordance across all three estimators is the strongest evidence of an identified causal effect under conditional exchangeability.

Usage

morie_otis_causal_grid(df = NULL, seed = 123L)

Arguments

df

OTIS placement-level data.frame. If NULL, attempts to resolve via morie_otis_load().

seed

Integer seed for the cross-fitting (default 123).

Value

Data.frame with columns pair, estimator, n, p_treat, ate, ate_se, ate_pval, ci95_lo, ci95_hi, notes.

Examples

if (FALSE) { # \dontrun{
  morie_otis_causal_grid()
} # }