Merge pull request #245 from igerber/staggered-ddd

Add Staggered Triple Difference estimator

Author: igerber

ROADMAP.md

@@ -30,7 +30,7 @@ Extend the existing `TripleDifference` estimator to handle staggered adoption se
 - Event study aggregation and pre-treatment placebo effects
 - Multiplier bootstrap for valid inference in staggered settings
 
-**Reference**: [Ortiz-Villavicencio & Sant'Anna (2025)](https://arxiv.org/abs/2505.09942). *Working Paper*. R package: `triplediff`.
+**Reference**: [Ortiz-Villavicencio & Sant'Anna (2025)](https://arxiv.org/abs/2505.09942). "Better Understanding Triple Differences Estimators." *Working Paper*. R package: `triplediff`.
 
 ### Enhanced Visualization
 

TODO.md

@@ -59,6 +59,9 @@ Deferred items from PR reviews that were not addressed before merge.
 | TripleDifference power: `generate_ddd_data` is a fixed 2×2×2 cross-sectional DGP — no multi-period or unbalanced-group support. | `prep_dgp.py`, `power.py` | #208 | Low |
 | Survey design resolution/collapse patterns inconsistent across panel estimators — extract shared helpers for panel-to-unit collapse, post-filter re-resolution, metadata recomputation | `continuous_did.py`, `efficient_did.py`, `stacked_did.py` | #226 | Low |
 | TROP: `fit()` and `_fit_global()` share ~150 lines of near-identical data setup. Extract shared helpers to eliminate cross-file sync risk. | `trop.py`, `trop_global.py`, `trop_local.py` | — | Low |
+| StaggeredTripleDifference R cross-validation: CSV fixtures not committed (gitignored); tests skip without local R + triplediff. Commit fixtures or generate deterministically. | `tests/test_methodology_staggered_triple_diff.py` | #245 | Medium |
+| StaggeredTripleDifference R parity: benchmark only tests no-covariate path (xformla=~1). Add covariate-adjusted scenarios and aggregation SE parity assertions. | `benchmarks/R/benchmark_staggered_triplediff.R` | #245 | Medium |
+| StaggeredTripleDifference: per-cohort group-effect SEs include WIF (conservative vs R's wif=NULL). Documented in REGISTRY. Could override mixin for exact R match. | `staggered_triple_diff.py` | #245 | Low |
 
 #### Performance
 

benchmarks/R/benchmark_staggered_triplediff.R

@@ -0,0 +1,147 @@
+#!/usr/bin/env Rscript
+# Benchmark: Staggered Triple Difference (R `triplediff` package)
+#
+# Generates golden values for cross-validation against Python
+# StaggeredTripleDifference estimator.
+#
+# Usage:
+#   Rscript benchmark_staggered_triplediff.R
+
+library(triplediff)
+library(jsonlite)
+library(data.table)
+
+cat("=== Staggered DDD Benchmark Generator ===\n")
+
+output_dir <- file.path(dirname(dirname(getwd())), "benchmarks", "data", "synthetic")
+# Handle running from project root or benchmarks/R
+if (!dir.exists(output_dir)) {
+  output_dir <- "benchmarks/data/synthetic"
+}
+if (!dir.exists(output_dir)) {
+  dir.create(output_dir, recursive = TRUE)
+}
+
+results <- list()
+
+# Scenario definitions
+scenarios <- list(
+  list(seed=42,  dgp=1, method="dr",  cg="nevertreated",   key="s42_dgp1_dr_nt"),
+  list(seed=42,  dgp=1, method="ipw", cg="nevertreated",   key="s42_dgp1_ipw_nt"),
+  list(seed=42,  dgp=1, method="reg", cg="nevertreated",   key="s42_dgp1_reg_nt"),
+  list(seed=42,  dgp=1, method="dr",  cg="notyettreated",  key="s42_dgp1_dr_nyt"),
+  list(seed=42,  dgp=1, method="ipw", cg="notyettreated",  key="s42_dgp1_ipw_nyt"),
+  list(seed=42,  dgp=1, method="reg", cg="notyettreated",  key="s42_dgp1_reg_nyt"),
+  list(seed=123, dgp=1, method="dr",  cg="nevertreated",   key="s123_dgp1_dr_nt"),
+  list(seed=123, dgp=1, method="dr",  cg="notyettreated",  key="s123_dgp1_dr_nyt"),
+  list(seed=99,  dgp=1, method="dr",  cg="nevertreated",   key="s99_dgp1_dr_nt"),
+  list(seed=99,  dgp=1, method="dr",  cg="notyettreated",  key="s99_dgp1_dr_nyt")
+)
+
+for (sc in scenarios) {
+  cat(sprintf("  Running scenario: %s ...\n", sc$key))
+
+  set.seed(sc$seed)
+  dgp <- gen_dgp_mult_periods(size = 500, dgp_type = sc$dgp)
+  data <- dgp$data
+
+  # Save data CSV (one per seed+dgp combo, reused across methods)
+  data_key <- sprintf("s%d_dgp%d", sc$seed, sc$dgp)
+  csv_path <- file.path(output_dir, sprintf("staggered_ddd_data_%s.csv", data_key))
+  if (!file.exists(csv_path)) {
+    fwrite(data, csv_path)
+    cat(sprintf("    Saved data: %s\n", csv_path))
+  }
+
+  # Run DDD estimation
+  res <- tryCatch({
+    ddd(yname = "y", tname = "time", idname = "id",
+        gname = "state", pname = "partition",
+        xformla = ~1,  # no covariates for cross-validation
+        data = data,
+        control_group = sc$cg,
+        base_period = "varying",
+        est_method = sc$method,
+        panel = TRUE)
+  }, error = function(e) {
+    cat(sprintf("    ERROR: %s\n", e$message))
+    return(NULL)
+  })
+
+  if (is.null(res)) next
+
+  # Group-time results
+  gt_results <- data.frame(
+    group = res$groups,
+    period = res$periods,
+    att = res$ATT,
+    se = res$se
+  )
+
+  # Event study aggregation
+  agg_es <- tryCatch({
+    agg_ddd(res, type = "eventstudy")
+  }, error = function(e) {
+    cat(sprintf("    Event study agg failed: %s\n", e$message))
+    NULL
+  })
+
+  es_results <- NULL
+  overall_att_es <- NA
+  overall_se_es <- NA
+  if (!is.null(agg_es)) {
+    a <- agg_es$aggte_ddd
+    es_results <- data.frame(
+      event_time = a$egt,
+      att = a$att.egt,
+      se = a$se.egt
+    )
+    overall_att_es <- a$overall.att
+    overall_se_es <- a$overall.se
+  }
+
+  # Simple aggregation
+  agg_simple <- tryCatch({
+    agg_ddd(res, type = "simple")
+  }, error = function(e) {
+    cat(sprintf("    Simple agg failed: %s\n", e$message))
+    NULL
+  })
+
+  overall_att_simple <- NA
+  overall_se_simple <- NA
+  if (!is.null(agg_simple)) {
+    a <- agg_simple$aggte_ddd
+    overall_att_simple <- a$overall.att
+    overall_se_simple <- a$overall.se
+  }
+
+  # Store results
+  results[[sc$key]] <- list(
+    seed = sc$seed,
+    dgp_type = sc$dgp,
+    est_method = sc$method,
+    control_group = sc$cg,
+    n = res$n,
+    gt_att = as.list(gt_results$att),
+    gt_se = as.list(gt_results$se),
+    gt_groups = as.list(gt_results$group),
+    gt_periods = as.list(gt_results$period),
+    overall_att_simple = overall_att_simple,
+    overall_se_simple = overall_se_simple,
+    overall_att_es = overall_att_es,
+    overall_se_es = overall_se_es,
+    es_event_times = if (!is.null(es_results)) as.list(es_results$event_time) else NULL,
+    es_att = if (!is.null(es_results)) as.list(es_results$att) else NULL,
+    es_se = if (!is.null(es_results)) as.list(es_results$se) else NULL
+  )
+
+  cat(sprintf("    GT ATT: %s\n", paste(round(res$ATT, 4), collapse=", ")))
+  cat(sprintf("    Overall ATT (simple): %.4f\n", overall_att_simple))
+}
+
+# Save all results as JSON
+json_path <- file.path(output_dir, "staggered_ddd_r_results.json")
+writeLines(toJSON(results, auto_unbox = TRUE, pretty = TRUE, digits = 10), json_path)
+cat(sprintf("\nResults saved to: %s\n", json_path))
+cat("Done.\n")