| { |
| "schema_version": "1.0", |
| "title": "Reproduction: Robust Sequential Experimental Design for A/B Testing", |
| "emoji": "🔬", |
| "space_id": "snaykey/repro-robust-seq-ab-testing", |
| "paper": { |
| "arxiv_id": null, |
| "openreview_id": "Q7lEZjtDKO" |
| }, |
| "tags": [ |
| "icml2026-repro", |
| "paper-Q7lEZjtDKO" |
| ], |
| "updated_at": "2026-07-29T00:00:00+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: Robust Sequential Experimental Design for A/B Testing", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1", |
| "title": "Theorem 1 formulates the optimal sequential treatment allocation problem in contextual bandits as a dynamic program with Markov state (Delta_i, Gamma_i) representing covariate imbalance statistics (Theorem 1, Section 3).", |
| "file": "pages/claim-1/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2", |
| "title": "Theorem 2 and Theorem 3 extend the dynamic-programming formulation to dynamic settings with carryover effects, decomposing the problem into an across-day Bellman recursion over N days and a within-day finite-horizon MDP solvable via reinforcement learning (Theorem 2, Theorem 3, Section 4).", |
| "file": "pages/claim-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3", |
| "title": "The proposed robust sequential design (RSD) bounds the worst-case mean squared error of the estimated treatment effect under model misspecification via an orthogonalization technique, remaining valid when the true outcome model contains unknown nonlinear components (Section 3).", |
| "file": "pages/claim-3/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4", |
| "title": "In synthetic contextual bandit experiments with both additive and interactive treatment effects, and in dynamic settings with time horizons T in {6, 12} using 400-1000 simulation replications, RSD consistently outperforms random assignment, switchback designs, and Neyman-balanced procedures (Section 5).", |
| "file": "pages/claim-4/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5", |
| "title": "RSD is evaluated on real-world data from a ride-sharing company in addition to synthetic experiments, demonstrating effectiveness beyond simulation (Section 5).", |
| "file": "pages/claim-5/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| } |
| } |
