Validation, NCE analysis, per-replicate oracle for landmark/naive, RCT truths script, resumable experiment runner

- W0 harm validation (40 reps): CCW and sequential ITT unbiased for their truths (|z| <= 0.7)
- PVI models fitted separately inside and after the window
- experiments.yml grid (8 worlds x 3 scenarios; designs x V1-V4; NCE; RCTs); smoke run clean

Co-Authored-By: Claude Opus 5.5 <[email protected]>

simulations/03_limits.R

@@ -0,0 +1,20 @@
+# RCT ITT truths for every world x scenario (the landmark and naive own estimands are
+# estimated within replicates by run_oracle()). Writes simulations/truths/truths_rct.csv.
+# Usage: Rscript simulations/03_limits.R [n] [cores]
+pkgload::load_all("pvisim", quiet = TRUE)
+suppressPackageStartupMessages(library(data.table))
+a <- commandArgs(TRUE)
+n <- if (length(a) >= 1) as.numeric(a[1]) else 5e5
+cores <- if (length(a) >= 2) as.integer(a[2]) else 6L
+raw <- yaml::read_yaml(default_params_path())
+jobs <- CJ(world = names(raw$worlds), scenario = names(raw$scenarios), sorted = FALSE)
+res <- parallel::mclapply(seq_len(nrow(jobs)), function(i) {
+  w <- jobs$world[i]; s <- jobs$scenario[i]
+  par <- load_params(w, s)
+  rbind(rct_truth(par, "realistic", n = 1e6)[outcome %in% c("male", "disease", "nco1", "nco2")],
+        rct_truth(par, "ideal", n = 1e6)[outcome %in% c("male", "disease", "nco1", "nco2")])
+}, mc.cores = cores)
+bad <- vapply(res, inherits, TRUE, "try-error")
+if (any(bad)) stop(res[bad][[1]])
+fwrite(rbindlist(res), "simulations/truths/truths_rct.csv")
+cat("wrote simulations/truths/truths_rct.csv\n")