RRSC — Robust-Regression Synthetic Control with Interference (He, Li, Shi & Miao 2026)

RRSC — Robust-Regression Synthetic Control with Interference (He, Li, Shi & Miao 2026)#

Estimator:

Robust-Regression Synthetic Control with Interference (RRSC) — mlsynth.RRSC

Source:

He, P., Li, Y., Shi, X. and Miao, W. (2026), A robust regression approach to synthetic control with interference (arXiv:2411.01249).

Replication type:

Cross-validation — matched value-for-value against the authors’ released reference R code, in both regimes, on both empirical applications.

Status:

Fully verified — point estimates value-for-value; bootstrap intervals within Monte-Carlo tolerance.

Validation strategy#

The paper ships a full reproducibility package: the reference R for both regimes (the fixed-N FixN code and the large-N LargeN code, the latter reusing the factor-analysis routines of Wang et al. 2017), the two empirical datasets, and the authors’ saved outputs. mlsynth’s port is checked against a live run of that R – first confirming the R reproduces the authors’ saved outputs, then matching the Python port to the R cell by cell.

Two facts make a clean value-for-value comparison possible and are worth stating because a naive re-implementation would miss them:

  • The large-N EM factor analysis stops at R’s default relative tolerance (tol = 1e-6), which on the Beijing panel is an early stop – roughly 25 iterations, far short of the maximum-likelihood optimum (about 1,169). The authors’ published effects depend on that early stop, so mlsynth’s fa_em() transcribes the reference’s exact per-iteration algebra (including its non-standard loading update) to reproduce the same stopping point.

  • The fixed-N LTS uses robustbase::ltsReg’s finite-sample raw-scale correction (Pison, Van Aelst and Willems 2002). mlsynth transcribes that correction (lts_fit()), so the reweighted coefficients match ltsReg rather than an asymptotic approximation.

Large-N — Beijing air pollution#

Beijing’s orange-alert pollution policy (November 2016) is evaluated on an hourly station panel: 108 monitoring stations, 48 pre-alert and 24 post-alert hours, with \(r = 7\) factors chosen by the Bai and Ng criterion. The estimand is the per-station effect – a reduction in the treated core-Beijing stations and, downwind, a positive interference effect consistent with the prevailing wind.

Running the authors’ Beijing_analysis.R live reproduces their saved output exactly (108 station effects to machine zero; p-values to the third decimal, the resolution of the saved file). mlsynth’s large-N port reproduces the live-R station effects to max |Δ| ≈ 2e-12 – value-for-value. The bootstrap p-values agree within Monte-Carlo tolerance (the circular block bootstrap draws differ between R’s and NumPy’s random-number streams), and the set of significant stations, and their signs, agree.

Fixed-N — U.S. embassy relocation to Jerusalem#

The December 2017 announcement relocating the U.S. embassy is evaluated on a weekly conflict-event panel of nine Middle East units (Israel-Palestine treated, 98 pre and 48 post weeks, \(r = 2\)). The headline is a significant interference effect on Jordan.

Running the authors’ MiddleEast_analysis.R live reproduces their saved MiddleEast_results.rds to max |Δ| ≈ 2e-12 on the point estimates and ≈ 5e-11 on the confidence intervals (the bootstrap is seed-deterministic in R). mlsynth’s fixed-N port reproduces the point estimates to max |Δ| ≈ 3e-4 – the residual is the factor-analysis optimiser tolerance, tightened by lowering ftol. Every seam matches the reference: the factanal uniquenesses to 1e-5, the raw LTS subset and coefficients exactly, the Donoho-Johnstone valid-control selection exactly, and Jordan’s interference effect at 7.999 with a 95% interval excluding zero.

Honest caveats#

  • Point estimates are deterministic and match value-for-value; the CBB confidence intervals and p-values are Monte-Carlo objects and match only within bootstrap tolerance, because the resampling uses NumPy’s random stream rather than R’s sample().

  • The counterfactual for a treated unit that is nearly orthogonal to the common factors (low communality) is close to a raw before-after difference; RRSC surfaces this as a diagnostic rather than hiding it. For Israel-Palestine the two common factors explain only about 3% of the pre-period variance, so its direct-effect estimate is essentially a level shift with a small factor adjustment – a limitation of the data, not the port.

Durable benchmark#

The cross-validation is captured in benchmarks/cases/rrsc_reference.py, which runs the reference R (when available) and asserts the Python port matches it; it reports SKIP when R or the reference package is absent, so the suite never turns red on a machine without the toolchain.