MAREX — Abadie & Zhao (2026) Walmart design (independent validator)

Independent, commit-stamped validator of MAREX – mlsynth’s port of Abadie & Zhao’s synthetic-control experimental-design estimator – on the paper’s Section 4 Walmart application. The authors’ reference code is the R package jinglongzhao2/SCDesign.

This complements LEXSCM — Synthetic Experimental Design (Abadie-Zhao 2026; Vives-i-Bastida 2022), which validates the same Abadie-Zhao design on the same Walmart panel through the lexicographic LEXSCM solver; here the check runs MAREX’s own MIQP optimizer.

Data

basedata/walmart_weekly_sales.csv (45 stores x 143 weeks; value-identical to SCDesign’s Walmart.csv), restricted to the first 10 stores so the exact mixed-integer program is fast and deterministic.

Result — placebo design (Sec. 4)

A placebo intervention (week 129, no real effect) must yield a design whose synthetic treated and control units track closely pre-period and whose estimated effect is indistinguishable from zero. MAREX’s exact MIQP delivers exactly that:

– the paper’s “no spurious effect” result, and the ~2.7% pre-fit tracking matches LEXSCM on this panel.

Note

Exact MIQP, not the relaxation. The benchmark uses MAREX’s exact MIQP (free SCIP backend). The relaxed continuous-z mode shares A&Z’s objective (build_objective is common to both) but drops the integrality that defines the selection; for a small treated count the relaxed optimum is degenerate, so its top-m rounding is lossy and numerically non-deterministic – unfaithful to the paper’s exact design. The authors’ R solves the full 45-store MIQP with Gurobi (a licence-free environment cannot run it), so this validator is Path A on a subset with mlsynth’s own solver rather than a live R cross-validation; only SCDesign’s quadprog SC-weight solver is open.

Reproduce

python benchmarks/run_benchmarks.py marex_walmart