diff --git a/CHANGELOG.md b/CHANGELOG.md index 9d6f1ed3..01752691 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -30,8 +30,22 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 mislabeled cell can never publish; the legacy `--type twfe` no-op on the BasicDiD scripts now fails loudly (absorbed-FE TWFE has its own dedicated script pair, and the refresh benchmarks it separately from BasicDiD). - Regenerated page numbers land together with the committed results - artifact once the timed refresh run completes. + The full gated run is committed (`results/refresh_results.json`) and + `docs/benchmarks.rst` is regenerated from it: CallawaySantAnna is + 4.6-15.5x faster than R `did` with the advantage GROWING with scale + (15.5x at 10k units / 150k observations, 14.8x at 20k units / 360k + observations; the previously published numbers showed the advantage + shrinking); SyntheticDiD's Rust backend is + 18-55x faster than R `synthdid` at matched 200-replication placebo + variance (the previously published table - captured before the current + Frank-Wolfe kernels and at unequal placebo-replication counts - showed Rust + SLOWER than pure Python); MPDTA real-data validation is ~5x faster with + exact ATT match. Honest regressions from the fairness fixes are + published as-is: warmed-up fixest now beats the sub-35ms BasicDiD/TWFE + interaction-OLS cells at 10k-20k (0.6-0.9x), and pure-Python + SyntheticDiD at equal placebo-replication counts is 0.5-4.4x vs R (the old + 2.4-16.5x claim reflected a 50-vs-200 replication asymmetry). All + 20 cells pass every publication gate with zero flags. ### Fixed - **`docs/benchmarks.rst` SDID weight-parity claim now machine-verified (and diff --git a/TODO.md b/TODO.md index a6872c79..5fa50d47 100644 --- a/TODO.md +++ b/TODO.md @@ -45,13 +45,6 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m ### Testing / docs -| Issue | Location | Origin | Effort | Priority | -|-------|----------|--------|--------|----------| -| Benchmark refresh phase 2 (same PR #672): run the gated timed refresh on an idle machine, commit `benchmarks/refresh_2026_07/results/refresh_results.json`, regenerate the marker-bounded regions of `docs/benchmarks.rst` via `gen_benchmark_tables.py`, and reconcile the remaining pre-refresh prose (protocol bullets "3 replications / mean ± std", combined BasicDiD/TWFE wording, SDID note under the perf table, Key Observations, "Reproducing Benchmarks" section, `llms.txt` speedup cross-references). Row removed by the phase-2 push itself. | `benchmarks/refresh_2026_07/`, `docs/benchmarks.rst` | #672 | Mid | Medium | - -| Issue | Location | Origin | Effort | Priority | -|-------|----------|--------|--------|----------| - --- ## Deferred / Documented diff --git a/benchmarks/refresh_2026_07/results/refresh_results.json b/benchmarks/refresh_2026_07/results/refresh_results.json new file mode 100644 index 00000000..a4134d21 --- /dev/null +++ b/benchmarks/refresh_2026_07/results/refresh_results.json @@ -0,0 +1,7269 @@ +{ + "cells": { + "basic/small": { + "estimator": "BasicDiD", + "scale": "small", + "data_file": "basic_small.csv", + "data_sha256": "96d0fc260b04134ecc40e530fca8fc21dac1b22f2e3609d06b4bf25a6ea3baf3", + "n_reps_requested": 8, + "captured_at_utc": "2026-07-10T22:59:43+00:00", + "loadavg_at_cell": [ + 3.5078125, + 2.99560546875, + 2.9375 + ], + "flags": [], + "python_pure": { + "result": { + "estimator": "diff_diff.DifferenceInDifferences", + "backend": "python", + "cluster": 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"median": 3.5420220000087284, + "min": 3.441547249996802, + "max": 3.6028128750040196 + } + }, + "cv": 0.023079421018276668, + "att_spread": 0.0, + "n_nonfinite_att": 0, + "n_nonfinite_se": 0, + "se_values": [ + 0.006636122895643467, + 0.006636122895643467, + 0.006636122895643467, + 0.006636122895643467 + ], + "flags": [] + }, + "3.7.0": { + "result": { + "estimator": "diff_diff.MultiPeriodDiD", + "backend": "rust", + "cluster": "unit", + "att": 3.008600615793982, + "se": 0.006636122895643467, + "pvalue": 0.0, + "ci_lower": 2.9955940456357455, + "ci_upper": 3.0216071859522184, + "reference_period": 10, + "timing": { + "estimation_seconds": 2.995273000007728, + "total_seconds": 2.995273000007728 + }, + "metadata": { + "n_units": 100000, + "n_periods": 20, + "n_obs": 2000000, + "n_pre": 10, + "n_post": 10, + "warmup": true + }, + "provenance": { + "diff_diff_version": "3.7.0", + "diff_diff_path": ".../python3.14/site-packages/diff_diff/__init__.py", + "has_rust_backend": true, + "rust_backend_info": { + "blas": true, + "openblas": false, + "accelerate": true + }, + "python_version": "3.14.4", + "python_executable": ".../venvs/dd370/bin/python", + "numpy_version": "2.5.1", + "pandas_version": "3.0.3", + "diff_diff_env": { + "DIFF_DIFF_BENCH_USE_INSTALLED": "1" + } + }, + "n_period_effects": 19 + }, + "timing": { + "raw_timings": [ + 2.9768296669935808, + 2.963059458998032, + 3.0497124169924064, + 2.995273000007728 + ], + "n_reps": 3, + "first_run_excluded": true, + "first_run_seconds": 2.9768296669935808, + "stats": { + "mean": 3.002681625332722, + "std": 0.04379896778690467, + "median": 2.995273000007728, + "min": 2.963059458998032, + "max": 3.0497124169924064 + } + }, + "cv": 0.014586617314798194, + "att_spread": 0.0, + "n_nonfinite_att": 0, + "n_nonfinite_se": 0, + "se_values": [ + 0.006636122895643467, + 0.006636122895643467, + 0.006636122895643467, + 0.006636122895643467 + ], + "flags": [] + }, + "att_gap_old_new": 0.0 + }, + "callaway/story2m": { + "estimator": "CallawaySantAnna", + "scale": "story2m", + "data_file": "staggered_story2m.csv", + "data_sha256": "2013657d9afdff7ef0d81b618adf5364d842c0af8e4955aa025c1b70d346447f", + "flags": [], + "3.5.3": { + "result": { + "estimator": "diff_diff.CallawaySantAnna", + "backend": "rust", + "method": "dr", + "control_group": "never_treated", + "overall_att": 3.384769412682571, + "overall_se": 0.0024002541143586186, + "timing": { + "estimation_seconds": 1.3185443330003181, + "total_seconds": 1.3185443330003181 + }, + "metadata": { + "n_units": 100000, + "n_periods": 20, + "n_obs": 2000000, + "warmup": true + }, + "provenance": { + "diff_diff_version": "3.5.3", + "diff_diff_path": ".../python3.14/site-packages/diff_diff/__init__.py", + "has_rust_backend": true, + "rust_backend_info": { + "blas": true, + "accelerate": true, + "openblas": false + }, + "python_version": "3.14.4", + "python_executable": ".../venvs/dd353/bin/python", + "numpy_version": "2.5.1", + "pandas_version": "3.0.3", + "diff_diff_env": { + "DIFF_DIFF_BENCH_USE_INSTALLED": "1" + } + }, + "n_group_time_effects": 152, + "n_event_study": 26, + "n_group_effects": 8 + }, + "timing": { + "raw_timings": [ + 1.3341362079954706, + 1.3351893340004608, + 1.3502373329974944, + 1.3185443330003181 + ], + "n_reps": 3, + "first_run_excluded": true, + "first_run_seconds": 1.3341362079954706, + "stats": { + "mean": 1.3346569999994244, + "std": 0.015853204623095852, + "median": 1.3351893340004608, + "min": 1.3185443330003181, + "max": 1.3502373329974944 + } + }, + "cv": 0.011878111472163026, + "att_spread": 0.0, + "n_nonfinite_att": 0, + "n_nonfinite_se": 0, + "se_values": [ + 0.0024002541143586186, + 0.0024002541143586186, + 0.0024002541143586186, + 0.0024002541143586186 + ], + "flags": [] + }, + "3.7.0": { + "result": { + "estimator": "diff_diff.CallawaySantAnna", + "backend": "rust", + "method": "dr", + "control_group": "never_treated", + "overall_att": 3.384769412682571, + "overall_se": 0.002400254114358619, + "timing": { + "estimation_seconds": 0.28190691699273884, + "total_seconds": 0.28190691699273884 + }, + "metadata": { + "n_units": 100000, + "n_periods": 20, + "n_obs": 2000000, + "warmup": true + }, + "provenance": { + "diff_diff_version": "3.7.0", + "diff_diff_path": ".../python3.14/site-packages/diff_diff/__init__.py", + "has_rust_backend": true, + "rust_backend_info": { + "accelerate": true, + "blas": true, + "openblas": false + }, + "python_version": "3.14.4", + "python_executable": ".../venvs/dd370/bin/python", + "numpy_version": "2.5.1", + "pandas_version": "3.0.3", + "diff_diff_env": { + "DIFF_DIFF_BENCH_USE_INSTALLED": "1" + } + }, + "n_group_time_effects": 152, + "n_event_study": 26, + "n_group_effects": 8 + }, + "timing": { + "raw_timings": [ + 0.2766472499934025, + 0.27416658400034066, + 0.2570516670093639, + 0.28190691699273884 + ], + "n_reps": 3, + "first_run_excluded": true, + "first_run_seconds": 0.2766472499934025, + "stats": { + "mean": 0.2710417226674811, + "std": 0.012718861257841276, + "median": 0.27416658400034066, + "min": 0.2570516670093639, + "max": 0.28190691699273884 + } + }, + "cv": 0.04692584275464116, + "att_spread": 0.0, + "n_nonfinite_att": 0, + "n_nonfinite_se": 0, + "se_values": [ + 0.002400254114358619, + 0.002400254114358619, + 0.002400254114358619, + 0.002400254114358619 + ], + "flags": [] + }, + "att_gap_old_new": 0.0 + } + }, + "run_metadata": { + "date_utc": "2026-07-11T00:59:47+00:00", + "hardware": { + "cpu": "Apple M4 Max", + "memory_gb": 36, + "os": "macOS 26.5.2", + "arch": "arm64" + }, + "orchestrator_python": "3.14.4", + "repo_git_sha": "a30765632c7770bcbf6aba83ca206b9bb539102e", + "r_version": "R version 4.5.2 (2025-10-31)", + "r_packages": { + "did": "2.5.1", + "fixest": "0.14.2", + "synthdid": "0.0.9", + "jsonlite": "2.0.0", + "data.table": "1.18.0" + }, + "thread_policy": "No arm is thread-restricted: R runs at fixest/data.table defaults; diff-diff wheels run at Accelerate/rayon defaults. Thread-count env vars (RAYON/OMP/OPENBLAS/VECLIB/MKL/data.table) are stripped from every benchmark subprocess and R runs under --vanilla (no user/site .Rprofile/.Renviron), so package defaults are enforced, not assumed. Per-arm thread counts are recorded in each result's metadata.", + "loadavg_at_start": [ + 3.78515625, + 3.63916015625, + 3.51708984375 + ], + "protocol": "Each replication is a fresh subprocess run strictly sequentially (one benchmark process on the machine at a time) with an untimed in-process warm-up fit before the timed fit. The first replication is additionally excluded from statistics. Published statistic: median of the counted replications. Arms with CV > 10% are rerun once and flagged if still noisy." + }, + "pins": { + "dd353": "3.5.3", + "dd370": "3.7.0" + } +} \ No newline at end of file diff --git a/benchmarks/refresh_2026_07/results/version_story.md b/benchmarks/refresh_2026_07/results/version_story.md new file mode 100644 index 00000000..a7da0af6 --- /dev/null +++ b/benchmarks/refresh_2026_07/results/version_story.md @@ -0,0 +1,46 @@ +# diff-diff optimization story: released 3.5.3 vs 3.7.0 + +**Headline: CallawaySantAnna runs ~4.9x faster on the +3.7.0 wheel than on 3.5.3** at every measured scale +(360,000 through 2,000,000 rows), with identical estimates. This is the +flagship staggered-adoption estimator and the primary target +of the June-July 2026 scaling arc (O(n_units) aggregation +influence functions, fused bootstrap, per-cell solver work). +The BasicDiD/MultiPeriodDiD rows below are supporting +context: those paths were already fast and saw incremental +gains at these benchmark shapes. + +Internal benchmark artifact (NOT part of the docs site). Same +benchmark scripts, same seed-42 data, two pinned PyPI wheels in +isolated venvs, wheel defaults (Rust + Accelerate backend, no +DIFF_DIFF_* env knobs), fresh subprocess per replication, strictly +sequential, untimed in-process warm-up, median of counted reps +(first rep excluded). + +v3.5.3 (2026-06-25) predates the June-July optimization arc; +v3.7.0 (2026-07-08) contains it (Rust demean_map FE-absorption +kernel, solve_ols marshalling slimming, CallawaySantAnna O(n_units) +aggregation + fused bootstrap, multiplier-bootstrap memory tiling). +The opt-in solve_ols Cholesky fast path (#670) is in NO released +wheel and plays no role in these numbers. SyntheticDiD is excluded: +its Frank-Wolfe rework shipped in v3.3.0, before both pins. + +| Estimator | Scale | Rows | 3.5.3 median (s) | 3.7.0 median (s) | Speedup | +|---|---|---|---|---|---| +| CallawaySantAnna | 20k | 360,000 | 0.242 | 0.048 | **5.02x** | +| CallawaySantAnna | story2m | 2,000,000 | 1.335 | 0.274 | **4.87x** | +| BasicDiD (interaction OLS) | 20k | 240,000 | 0.029 | 0.022 | **1.31x** | +| MultiPeriodDiD (absorbed FE) | 20k | 320,000 | 0.439 | 0.381 | **1.15x** | +| MultiPeriodDiD (absorbed FE) | story2m | 2,000,000 | 3.542 | 2.995 | **1.18x** | + +## Environment + +- Captured: 2026-07-11T00:59:47+00:00 +- Hardware: Apple M4 Max, 36 GB, macOS 26.5.2 (arm64) +- Wheels: diff-diff 3.5.3 and 3.7.0 (PyPI macosx-arm64, Rust backend + Apple Accelerate), python 3.14.4 +- Protocol: Each replication is a fresh subprocess run strictly sequentially (one benchmark process on the machine at a time) with an untimed in-process warm-up fit before the timed fit. The first replication is additionally excluded from statistics. Published statistic: median of the counted replications. Arms with CV > 10% are rerun once and flagged if still noisy. +- Thread policy: No arm is thread-restricted: R runs at fixest/data.table defaults; diff-diff wheels run at Accelerate/rayon defaults. Thread-count env vars (RAYON/OMP/OPENBLAS/VECLIB/MKL/data.table) are stripped from every benchmark subprocess and R runs under --vanilla (no user/site .Rprofile/.Renviron), so package defaults are enforced, not assumed. Per-arm thread counts are recorded in each result's metadata. + +Flags (if any) per cell: + +- none diff --git a/benchmarks/refresh_2026_07/run_version_story.py b/benchmarks/refresh_2026_07/run_version_story.py index 3a52de52..4c7f4aba 100644 --- a/benchmarks/refresh_2026_07/run_version_story.py +++ b/benchmarks/refresh_2026_07/run_version_story.py @@ -231,6 +231,51 @@ def write_report(payload: Dict[str, Any]) -> None: for k, fls in flagged.items(): lines.append(f"> - `{k}`: {', '.join(fls)}") lines.append("") + # Headline only when EVERY CallawaySantAnna cell is clean - a partially + # flagged run must not lead with "at every measured scale". The flag + # banner (below) also renders before the headline. + cs_all = {k: c for k, c in payload["cells"].items() if k.startswith("callaway/")} + cs = { + k: c + for k, c in cs_all.items() + if not c.get("flags") + and not c.get("3.5.3", {}).get("dropped") + and not c.get("3.7.0", {}).get("dropped") + and c.get("3.5.3") + and c.get("3.7.0") + } + if cs and len(cs) == len(cs_all): + speedups = [] + for c in cs.values(): + t_old = c["3.5.3"]["timing"]["stats"]["median"] + t_new = c["3.7.0"]["timing"]["stats"]["median"] + if t_new: + speedups.append(t_old / t_new) + rows_seen = [] + for c in cs.values(): + n_obs = c["3.5.3"]["result"].get("metadata", {}).get("n_obs") + if n_obs: + rows_seen.append(int(n_obs)) + if speedups and rows_seen: + lo, hi = min(speedups), max(speedups) + rng = f"{lo:.1f}x" if abs(hi - lo) < 0.25 else f"{lo:.1f}-{hi:.1f}x" + scale_txt = ( + f"({min(rows_seen):,} through {max(rows_seen):,} rows)" + if len(rows_seen) > 1 + else f"({rows_seen[0]:,} rows)" + ) + lines += [ + f"**Headline: CallawaySantAnna runs ~{rng} faster on the", + "3.7.0 wheel than on 3.5.3** at every measured scale", + f"{scale_txt}, with identical estimates. This is the", + "flagship staggered-adoption estimator and the primary target", + "of the June-July 2026 scaling arc (O(n_units) aggregation", + "influence functions, fused bootstrap, per-cell solver work).", + "The BasicDiD/MultiPeriodDiD rows below are supporting", + "context: those paths were already fast and saw incremental", + "gains at these benchmark shapes.", + "", + ] lines += [ "Internal benchmark artifact (NOT part of the docs site). Same", "benchmark scripts, same seed-42 data, two pinned PyPI wheels in", @@ -250,7 +295,11 @@ def write_report(payload: Dict[str, Any]) -> None: "| Estimator | Scale | Rows | 3.5.3 median (s) | 3.7.0 median (s) " "| Speedup |", "|---|---|---|---|---|---|", ] - for key, cell in payload["cells"].items(): + ordered = sorted( + payload["cells"].items(), + key=lambda kv: (0 if kv[0].startswith("callaway/") else 1, kv[0]), + ) + for key, cell in ordered: old = cell.get("3.5.3", {}) new = cell.get("3.7.0", {}) if old.get("dropped") or new.get("dropped") or not old or not new: diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt index 1c9ce3fd..388318ef 100644 --- a/diff_diff/guides/llms.txt +++ b/diff_diff/guides/llms.txt @@ -118,7 +118,7 @@ No R or Python package offers design-based variance estimation for modern hetero ## Optional -- [Rust Backend](https://diff-diff.readthedocs.io/en/stable/benchmarks.html): Optional Rust backend (`maturin develop --release`) for 5-50x speedups on Synthetic DiD, TROP, and other compute-intensive estimators +- [Rust Backend](https://diff-diff.readthedocs.io/en/stable/benchmarks.html): Rust backend (bundled in released wheels; `maturin develop --release` for source builds) for order-of-magnitude speedups on compute-intensive estimators - 18-55x vs R synthdid on Synthetic DiD in the published benchmarks - plus TROP and bootstrap acceleration - [Built-in Datasets](https://diff-diff.readthedocs.io/en/stable/api/datasets.html): Real-world datasets — Card & Krueger (1994), Castle Doctrine, divorce laws, MPDTA - [Visualization](https://diff-diff.readthedocs.io/en/stable/api/visualization.html): Event study plots, group effects, sensitivity plots, Bacon decomposition plots, power curves - [Data Preparation](https://diff-diff.readthedocs.io/en/stable/api/prep.html): Data generation, panel balancing, wide-to-long conversion, treatment/post indicator creation diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index 0388bd70..6215b4c1 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -6,8 +6,10 @@ Benchmarks ========== This document presents validation benchmarks comparing diff-diff against -established R packages for difference-in-differences analysis. As of v2.0.0, -diff-diff includes an optional Rust backend for accelerated computation. +established R packages for difference-in-differences analysis. Released +wheels bundle a Rust backend that is used automatically; the "Pure" arm in +the tables below runs the same wheel with the backend disabled +(``DIFF_DIFF_BACKEND=python``). .. contents:: Table of Contents :local: @@ -28,6 +30,9 @@ diff-diff is validated against the following R packages: * - ``DifferenceInDifferences`` - ``fixest::feols`` - Standard OLS with interaction + * - ``TwoWayFixedEffects`` + - ``fixest::feols`` (absorbed FE) + - Within-transformed unit + period fixed effects * - ``CallawaySantAnna`` - ``did::att_gt`` - Callaway & Sant'Anna (2021) @@ -50,7 +55,11 @@ Validation Approach 3. **JSON Interchange**: R scripts output JSON for comparison 4. **Automated Comparison**: Python script validates numerical equivalence 5. **Multiple Scales**: Test at small (200-400 obs), 1K, 5K, 10K, and 20K unit scales -6. **Replicated Timing**: 3 replications per benchmark to report mean ± std +6. **Replicated Timing**: multiple fresh-subprocess replications per benchmark + (8 for fast cells, 4 for the slow SyntheticDiD scales), each with an untimed + in-process warm-up fit; the first replication is excluded and the **median** + of the remaining replications is published (full distributions are retained + in the committed results artifact) 7. **Reproducible Seed**: Benchmarks use seed 42 for data generation 8. **Three-Way Comparison**: Compare R, Python (pure NumPy/SciPy), and Python (Rust backend) @@ -83,27 +92,41 @@ Summary Table - SE Rel Diff - CI Overlap - Status - * - BasicDiD/TWFE - - < 1e-10 + * - BasicDiD + - < 5e-11 - 0.0% - Yes - **PASS** + * - TWFE (absorbed FE) + - < 6e-12 + - 0.1% + - Yes + - **PASS** * - MultiPeriodDiD - - < 1e-11 + - < 2e-12 - 0.0% - Yes - **PASS** * - CallawaySantAnna - - < 1e-10 + - < 4e-11 - 0.0% - Yes - **PASS** * - SyntheticDiD - - < 1e-10 - - 0.3% + - < 4e-11 + - 11.5% - Yes - **PASS** +SyntheticDiD SE differences reflect Monte Carlo dispersion of the +placebo variance (R's placebo permutation is unseeded, so the two +implementations agree in distribution, not draw-by-draw): its SE is +gated at a 35% relative bound with R's rep-to-rep SE values recorded +in the committed results artifact, while the deterministic +Frank-Wolfe ATT is gated at 1e-8. All other estimators use analytical +SEs gated at the tolerances above. See the SyntheticDiD methodology +registry note (benchmark SE gate is Monte Carlo-bounded). + .. refresh-table-end: summary Basic DiD Results @@ -115,6 +138,7 @@ Basic DiD Results .. list-table:: :header-rows: 1 + :widths: 16 21 21 21 21 * - Metric - diff-diff (Pure) @@ -125,17 +149,17 @@ Basic DiD Results - 5.112 - 5.112 - 5.112 - - < 1e-10 + - < 4e-11 * - SE - 0.183 - 0.183 - 0.183 - 0.0% * - Time (s) - - 0.002 - - 0.002 - - 0.041 - - **22x faster** + - 0.0005 + - 0.0005 + - 0.0040 + - **7.6x faster** (rust) .. refresh-table-end: accuracy_basic @@ -150,6 +174,7 @@ MultiPeriodDiD Results .. list-table:: :header-rows: 1 + :widths: 16 21 21 21 21 * - Metric - diff-diff (Pure) @@ -160,22 +185,17 @@ MultiPeriodDiD Results - 2.912 - 2.912 - 2.912 - - < 1e-11 + - < 2e-12 * - SE - 0.158 - 0.158 - 0.158 - 0.0% - * - Period corr. - - 1.000 - - 1.000 - - (ref) - - Period max diff < 3e-11 * - Time (s) - - 0.005 - - 0.035 - - 0.035 - - **7x faster** (pure) + - 0.0040 + - 0.0040 + - 0.0056 + - **1.4x faster** (pure) .. refresh-table-end: accuracy_multiperiod @@ -194,6 +214,7 @@ Synthetic DiD Results .. list-table:: :header-rows: 1 + :widths: 16 21 21 21 21 * - Metric - diff-diff (Pure) @@ -204,17 +225,17 @@ Synthetic DiD Results - 3.840 - 3.840 - 3.840 - - < 1e-10 + - < 2e-11 * - SE - - 0.105 - - 0.099 - - 0.105 - - 0.3% (pure) + - 0.113 + - 0.113 + - 0.101 + - 11.5% * - Time (s) - - 3.41 - - 1.65 - - 8.19 - - **2.4x faster** (pure) + - 15.4 + - 0.139 + - 7.65 + - **55.0x faster** (rust) .. refresh-table-end: accuracy_synthdid @@ -226,10 +247,13 @@ with two-pass sparsification and auto-computed regularization (``zeta_omega``, SyntheticDiD methodology registry note). Both use placebo-based variance estimation (Algorithm 4 from Arkhangelsky et al. 2021). -The small SE difference (0.3% at small scale, up to ~7% at larger scales) is -due to Monte Carlo variance in the placebo procedure, which randomly permutes -control units to construct pseudo-treated groups. Different random seeds across -implementations produce slightly different placebo samples. +The SE difference (11.5% at small scale, ~3% at 1k/5k in this capture) is +Monte Carlo dispersion of the placebo procedure, which randomly permutes +control units to construct pseudo-treated groups: R's placebo permutation is +unseeded, so the two implementations agree in distribution rather than +draw-by-draw. R's rep-to-rep SE values are recorded in the committed results +artifact, and the benchmark gates this at a documented 35% bound (see the +SyntheticDiD methodology registry note). Callaway-Sant'Anna Results ~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -240,6 +264,7 @@ Callaway-Sant'Anna Results .. list-table:: :header-rows: 1 + :widths: 16 21 21 21 21 * - Metric - diff-diff (Pure) @@ -250,17 +275,17 @@ Callaway-Sant'Anna Results - 2.519 - 2.519 - 2.519 - - < 1e-10 + - < 3e-11 * - SE - 0.063 - 0.063 - 0.063 - 0.0% * - Time (s) - - 0.007 ± 0.000 - - 0.007 ± 0.000 - - 0.070 ± 0.001 - - **10x faster** + - 0.0028 + - 0.0028 + - 0.013 + - **4.6x faster** (pure) .. refresh-table-end: accuracy_callaway @@ -290,8 +315,10 @@ As of v2.1.0, event study SEs include the WIF adjustment matching R's Performance Comparison ---------------------- -We benchmarked performance across multiple dataset scales with 3 replications -each to provide mean ± std timing statistics. As of v2.0.0, we compare three +We benchmarked performance across multiple dataset scales. Reported timings +are medians of repeated fresh-subprocess replications with an untimed warm-up +fit on BOTH sides (R's byte-compiler JIT is kept out of the timing window; see +the environment block below for the full protocol). We compare three implementations: - **R**: Reference implementation (fixest, did packages) @@ -300,30 +327,39 @@ implementations: .. refresh-table-start: environment +.. rubric:: Benchmark environment (2026-07 refresh) + +- **Captured**: 2026-07-10T22:59:43+00:00 +- **Hardware**: Apple M4 Max, 36 GB RAM, macOS 26.5.2 (arm64) +- **diff-diff**: 3.7.0 released wheel from PyPI (Rust backend + Apple Accelerate), Python 3.14.4, NumPy 2.5.1, pandas 3.0.3 +- **R**: R version 4.5.2 (2025-10-31); did 2.5.1, fixest 0.14.2, synthdid 0.0.9 (installed at capture) +- **Threads**: No arm is thread-restricted: R runs at fixest/data.table defaults; diff-diff wheels run at Accelerate/rayon defaults. Thread-count env vars (RAYON/OMP/OPENBLAS/VECLIB/MKL/data.table) are stripped from every benchmark subprocess and R runs under --vanilla (no user/site .Rprofile/.Renviron), so package defaults are enforced, not assumed. Per-arm thread counts are recorded in each result's metadata. +- **Protocol**: Each replication is a fresh subprocess run strictly sequentially (one benchmark process on the machine at a time) with an untimed in-process warm-up fit before the timed fit. The first replication is additionally excluded from statistics. Published statistic: median of the counted replications. Arms with CV > 10% are rerun once and flagged if still noisy. + .. refresh-table-end: environment .. note:: - **v2.0.0 Rust Backend**: diff-diff v2.0.0 introduces an optional Rust backend - for accelerated computation. The Rust backend provides significant speedups - for **SyntheticDiD** (4-8x faster than pure Python), which uses custom Rust - implementations for synthetic weight computation and simplex projection. - For **BasicDiD** and **CallawaySantAnna**, the Rust backend provides minimal - additional speedup since these estimators primarily use OLS and variance - computations that are already highly optimized in NumPy/SciPy via BLAS/LAPACK. + **Rust Backend**: released wheels bundle the Rust backend (used + automatically). It is transformative for **SyntheticDiD** - 110x faster + than pure Python at small scale and 4-12x at 1k-5k in this capture, making + Rust the fastest implementation at every scale - via Gram-accelerated / + allocation-free Frank-Wolfe solvers and a batched placebo variance path. + For **BasicDiD**, **TWFE**, and **CallawaySantAnna**, Rust and pure Python + are near parity: those estimators are dominated by OLS/variance kernels + already optimized in NumPy/SciPy via BLAS/LAPACK. - As of v2.5.0, pre-built wheels on macOS and Linux link platform-optimized - BLAS libraries (Apple Accelerate and OpenBLAS respectively) for matrix-vector - and matrix-matrix products across all Rust-accelerated code paths. Windows - wheels continue to use pure Rust with no external dependencies. + Pre-built wheels on macOS and Linux link platform-optimized BLAS libraries + (Apple Accelerate and OpenBLAS respectively) across all Rust-accelerated + code paths. Windows wheels use pure Rust with no external dependencies. Three-Way Performance Summary ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -**BasicDiD/TWFE Results:** - .. refresh-table-start: perf_basic +**BasicDiD (interaction OLS, clustered):** + .. list-table:: :header-rows: 1 :widths: 12 15 18 18 12 12 @@ -332,38 +368,81 @@ Three-Way Performance Summary - R (s) - Python Pure (s) - Python Rust (s) + - Pure/R - Rust/R - - Rust/Pure * - small - - 0.034 - - 0.002 - - 0.002 - - **17x** - - 1.1x + - 0.0040 + - 0.0005 + - 0.0005 + - **7.5x** + - **7.6x** * - 1k - - 0.036 - - 0.003 - - 0.003 - - **13x** - - 1.0x + - 0.0042 + - 0.0011 + - 0.0010 + - **4.0x** + - **4.2x** * - 5k - - 0.042 - - 0.005 - - 0.006 - - **7x** - - 0.8x + - 0.0056 + - 0.0039 + - 0.0038 + - 1.4x + - 1.5x * - 10k - - 0.043 - - 0.010 + - 0.0077 + - 0.0086 + - 0.0086 + - 0.9x + - 0.9x + * - 20k + - 0.013 + - 0.020 + - 0.019 + - 0.7x + - 0.7x + +**TWFE (absorbed unit + post fixed effects, clustered):** + +.. list-table:: + :header-rows: 1 + :widths: 12 15 18 18 12 12 + + * - Scale + - R (s) + - Python Pure (s) + - Python Rust (s) + - Pure/R + - Rust/R + * - small + - 0.0046 + - 0.0011 + - 0.0012 + - **4.0x** + - **3.9x** + * - 1k + - 0.0049 + - 0.0020 + - 0.0019 + - **2.5x** + - **2.6x** + * - 5k + - 0.0072 + - 0.0066 + - 0.0058 + - 1.1x + - 1.2x + * - 10k + - 0.011 + - 0.014 - 0.012 - - **4x** - 0.8x - * - 20k - - 0.050 - - 0.022 - - 0.025 - - **2x** - 0.9x + * - 20k + - 0.020 + - 0.032 + - 0.027 + - 0.6x + - 0.7x .. refresh-table-end: perf_basic @@ -380,37 +459,37 @@ Three-Way Performance Summary - Python Pure (s) - Python Rust (s) - Pure/R - - Rust/Pure + - Rust/R * - small - - 0.069 - - 0.006 - - 0.007 - - **11x** - - 1.0x - * - 1k - - 0.119 - - 0.014 - 0.013 - - **9x** - - 1.0x + - 0.0028 + - 0.0028 + - **4.6x** + - **4.6x** + * - 1k + - 0.026 + - 0.0043 + - 0.0043 + - **6.0x** + - **6.0x** * - 5k - - 0.363 - - 0.055 - - 0.055 - - **7x** - - 1.0x + - 0.078 + - 0.0098 + - 0.0098 + - **8.0x** + - **8.0x** * - 10k - - 0.771 - - 0.146 - - 0.145 - - **5x** - - 1.0x + - 0.306 + - 0.020 + - 0.020 + - **15x** + - **15x** * - 20k - - 1.559 - - 0.366 - - 0.373 - - **4x** - - 1.0x + - 0.621 + - 0.042 + - 0.042 + - **15x** + - **15x** .. refresh-table-end: perf_callaway @@ -427,40 +506,42 @@ Three-Way Performance Summary - Python Pure (s) - Python Rust (s) - Pure/R - - Rust/Pure + - Rust/R * - small - - 8.19 - - 3.41 - - 1.65 - - **2.4x** - - **2.1x** + - 7.65 + - 15.4 + - 0.139 + - 0.5x + - **55x** * - 1k - - 111.7 - - 24.0 - - 76.1 - - **4.7x** - - 0.3x + - 101 + - 65.0 + - 5.59 + - 1.6x + - **18x** * - 5k - - 524.2 - - 31.7 - - 307.5 - - **16.5x** - - 0.1x + - 467 + - 106 + - 25.2 + - **4.4x** + - **19x** .. refresh-table-end: perf_synthdid .. note:: - **SyntheticDiD Performance**: diff-diff's pure Python backend achieves - **2.4x to 16.5x speedup** over R's synthdid package using the same - Frank-Wolfe optimization algorithm. At 5k scale, R takes ~9 minutes while - pure Python completes in 32 seconds. ATT estimates are numerically identical - (< 1e-10 difference) since both implementations use the same Frank-Wolfe - optimizer with two-pass sparsification. The Rust backend uses a - Gram-accelerated Frank-Wolfe solver for time weights (reducing per-iteration - cost from O(N×T0) to O(T0)) and an allocation-free solver for unit weights - (1 GEMV per iteration instead of 3, zero heap allocations). These - optimizations make the Rust backend faster than pure Python at all scales. + **SyntheticDiD Performance**: the Rust backend (the default in released + wheels) is **18-55x faster than R's synthdid** at matched 200-replication + placebo variance - at 5k scale R takes ~7.8 minutes while Rust completes + in 25 seconds. Pure Python ranges from 0.5x (small scale - slower than R) + to 4.4x (5k) at the same matched placebo-replication counts; earlier versions of this + page compared a 50-replication Python arm against R's 200 and overstated + the pure-Python advantage. ATT estimates are numerically identical + (< 1e-10) and unit/time weights reproduce R at < 1e-8 (id-aligned + comparison), since both implementations use the same Frank-Wolfe optimizer + with two-pass sparsification. The Rust backend uses a Gram-accelerated + Frank-Wolfe solver for time weights (per-iteration cost O(T0) instead of + O(N×T0)) and an allocation-free solver for unit weights. Dataset Sizes ~~~~~~~~~~~~~ @@ -506,41 +587,50 @@ Dataset Sizes - 20,000 × 60 - 240,000 - 1,200,000 +TWFE (absorbed FE) benchmarks reuse the BasicDiD datasets at every scale. + Key Observations ~~~~~~~~~~~~~~~~ -1. **Performance varies by estimator and scale**: - - - **BasicDiD/TWFE**: 2-17x faster than R at all scales - - **CallawaySantAnna**: 4-11x faster than R at all scales (vectorized WIF computation) - - **SyntheticDiD**: 2.4-16.5x faster than R (pure Python), with both - implementations using the same Frank-Wolfe algorithm - -2. **Rust backend benefit depends on the estimator**: - - - **SyntheticDiD**: Rust provides speedup at small scale (2.1x) but is - slower at larger scales due to placebo variance loop overhead - - **BasicDiD/CallawaySantAnna**: Rust provides minimal benefit (~1x) since - these estimators use OLS/variance computations already optimized in NumPy/SciPy - -3. **When to use Rust backend**: - - - **SyntheticDiD at small scale**: Rust is ~2x faster than pure Python - - **Bootstrap inference**: May help with parallelized iterations - - **BasicDiD/CallawaySantAnna**: Optional - pure Python is equally fast - -4. **Scaling behavior**: Python implementations show excellent scaling behavior - across all estimators. SyntheticDiD pure Python is 16.5x faster than R at - 5k scale. CallawaySantAnna achieves **exact SE accuracy** (0.0% difference) - while being 4-11x faster than R through vectorized NumPy operations. - -5. **No Rust required for most use cases**: Users without Rust/maturin can - install diff-diff and get full functionality with excellent performance. - Pure Python is the fastest option for SyntheticDiD at 1k+ scales. - -6. **CallawaySantAnna accuracy and speed**: As of v2.0.3, CallawaySantAnna +1. **Where diff-diff wins, the advantage grows with scale**: + + - **CallawaySantAnna**: 4.6x faster than R at small scale rising to + **15.5x at 10k units (150k observations) and 14.8x at 20k units + (360k observations)** - the 2026 scaling work (O(n_units) aggregation influence functions, fused + bootstrap, per-cell solver fast paths) pays off exactly where compute + matters, with exact SE parity (0.0% difference) maintained. + - **SyntheticDiD (Rust backend, the default install)**: **18-55x faster + than R** at matched placebo-replication counts. + +2. **The honest small-regression story**: BasicDiD and TWFE interaction/ + absorbed OLS cells all complete in under 35 milliseconds on both sides. + diff-diff is 2.5-7.6x faster up to 5k; at 10k-20k, warmed-up fixest is + 1.3-1.5x faster than us. At these absolute times the difference is + immaterial in practice - fixest is exceptionally well optimized for + simple regressions, and our performance work targets the estimators + where runtimes are measured in seconds or minutes. + +3. **Rust backend benefit depends on the estimator**: transformative for + SyntheticDiD (fastest implementation at every scale; 110x vs pure Python + at small scale) and near parity with pure Python for BasicDiD/TWFE/ + CallawaySantAnna, whose hot paths are already BLAS-bound in NumPy/SciPy. + Released wheels bundle Rust, so no toolchain is needed. + +4. **SyntheticDiD pure Python vs R at equal placebo-replication counts**: 0.5x (small, + slower than R) to 4.4x (5k). Earlier versions of this page compared + unequal placebo-replication counts and overstated the pure-Python + advantage; install the default wheel and the Rust backend makes the + question moot. + +5. **Every published number is gated**: ATT/SE tolerances per rendered arm, + CI overlap, per-period / per-(g,t) / event-study / group effect surfaces, + SyntheticDiD id-aligned weight identity, real-data known answers, and + replication determinism all hard-gate publication (a failed cell cannot + render). See the environment block above for the capture context. + +6. **CallawaySantAnna accuracy and speed**: CallawaySantAnna achieves both exact numerical accuracy (0.0% SE difference from R) AND - superior performance (4-10x faster than R) through vectorized weight + superior performance (4.6-15.5x faster than R) through vectorized weight influence function (WIF) computation using NumPy matrix operations. Performance Optimization Details @@ -549,7 +639,8 @@ Performance Optimization Details The performance improvements come from: 1. **Unified ``linalg.py`` backend**: Single optimized OLS/SE implementation - using scipy's gelsy LAPACK driver (QR-based, faster than SVD) + using an equilibrated SVD solve (gelsd-parity) with certified stage-0 + rank detection 2. **Vectorized cluster-robust SE**: Eliminated O(n × clusters) loop with pandas groupby aggregation @@ -571,7 +662,8 @@ The performance improvements come from: Why is diff-diff Fast? ~~~~~~~~~~~~~~~~~~~~~~ -1. **Optimized LAPACK**: scipy's gelsy driver for least squares +1. **Optimized LAPACK**: equilibrated SVD least squares with certified + rank detection 2. **Vectorized operations**: NumPy/pandas for matrix operations and aggregations 3. **Efficient memory access**: Pre-computed structures avoid repeated data reshaping 4. **Pure Python overhead minimized**: Hot paths use compiled NumPy/scipy routines @@ -614,13 +706,13 @@ Results Comparison - -0.039951 - **0** (exact match) * - SE (analytical) - - 0.0117 - - 0.0118 - - **< 1%** - * - Time (10 reps) - - 0.003s ± 0.000s - - 0.039s ± 0.006s - - **14.4x faster** + - 0.0120 + - 0.0120 + - **< 0.1%** + * - Time (median of 7) + - 0.0028s + - 0.013s + - **4.6x faster** .. refresh-table-end: mpdta @@ -633,9 +725,9 @@ Results Comparison weight influence function formula, achieving 0.0% difference from R's ``did`` package. Both point estimates and standard errors are numerically equivalent. -3. **Performance**: diff-diff is ~14x faster than R on this real-world dataset - at small scale. Performance scales differently at larger sizes (see performance - tables above). +3. **Performance**: diff-diff is ~5x faster than R on this real-world dataset + (both complete in milliseconds). Performance advantages grow with scale + (see the CallawaySantAnna performance table above). This validation on real-world data with known published results confirms that diff-diff produces correct estimates that match the reference R implementation. @@ -916,6 +1008,24 @@ Prerequisites Running Benchmarks ~~~~~~~~~~~~~~~~~~ +The published tables on this page are produced by the gated refresh harness +(fresh venv with the released wheel, warm-ups, medians, fail-closed +publication gates): + +.. code-block:: bash + + # One-time: create the pinned-wheel venv and preflight it + python benchmarks/refresh_2026_07/run_refresh.py --setup + + # Full gated run (idle machine recommended; SDID R cells dominate) + python benchmarks/refresh_2026_07/run_refresh.py + + # Regenerate the marker-bounded tables on this page from the results + python benchmarks/refresh_2026_07/gen_benchmark_tables.py + +The legacy harness below remains available for quick accuracy comparisons +(it is NOT the source of the published tables): + .. code-block:: bash # Run all benchmarks at small scale