{
  "paper": {
    "arxiv_id": "2608.10182",
    "title": "From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation",
    "url": "https://arxiv.org/abs/2608.10182",
    "organization": "LinkedIn"
  },
  "dataset": {
    "name": "MovieLens 100K causal targeting simulation",
    "users": 220,
    "items": 360
  },
  "setup": {
    "adapter": "incrementality",
    "same_budget_and_population": true
  },
  "baseline": {
    "name": "predictive targeting by estimated treated outcome",
    "policy_value": 0.09536825635811441,
    "total_incremental_value": 6.294304919635551,
    "budget_fraction": 0.3,
    "uplift_rank_correlation": 0.2307013774163521
  },
  "method": {
    "name": "uplift + uncertainty with global budget",
    "policy_value": 0.10407967501332929,
    "total_incremental_value": 6.869258550879733,
    "budget_fraction": 0.3,
    "uplift_rank_correlation": 0.5715856075559105
  },
  "relative": {
    "policy_value_percent": 9.134505534528072
  },
  "stages": {
    "examples": 220,
    "allocated_users": 66,
    "mean_propensity": 0.433110027582525,
    "treatment_rate": 0.41818181818181815,
    "uplift_rmse": 0.01841951085332956
  },
  "paper_results": {
    "linkedin_long_term_value_lift_percent": 7.2,
    "p_value": 0.041
  },
  "scope": "实际执行有混杂的 treatment 日志构造、双 outcome 回归、个体 uplift、不确定性探索分数和固定预算全局分配；未复刻 LinkedIn Transformer/DragonNet、在线神经 bandit 服务和大规模 LP 基础设施。",
  "schema_version": 2,
  "manifest": {
    "adapter_key": "incrementality",
    "arxiv_id": "2608.10182",
    "title": "From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation",
    "paper_url": "https://arxiv.org/abs/2608.10182",
    "track": "recommendation",
    "organization": "LinkedIn",
    "published": "2026-08-10",
    "code_url": null,
    "topics": [
      "causal-recommendation",
      "uplift",
      "bandit",
      "constrained-allocation"
    ],
    "local_code_dir": "src/auto_research/reproductions/incrementality",
    "fidelity": "core_mechanism",
    "evaluation_tier": "l2_public_dataset",
    "datasets": [
      "MovieLens 100K causal targeting simulation"
    ],
    "baseline": "predictive treated-outcome targeting at the same budget",
    "metrics": [
      "policy_value",
      "total_incremental_value",
      "uplift_rank_correlation"
    ],
    "default_seeds": [
      42,
      43,
      44
    ],
    "budget": "220 users; 30% treatment budget",
    "device_capabilities": [
      "cpu",
      "mps",
      "cuda"
    ],
    "requires_gpu_validation": false,
    "gpu_validation_artifact": null,
    "online_evidence": [
      {
        "product": "LinkedIn Feed marketing traffic",
        "metric": "primary long-term-value KPI",
        "lift_percent": 7.2,
        "traffic": "online A/B test",
        "source_url": "https://arxiv.org/html/2608.10182v1",
        "source_location": "Section 4 online evaluation",
        "significance": "p = 0.041",
        "retrieved_at": "2026-09-01"
      }
    ],
    "selection_exception": null,
    "evolve_operators": []
  },
  "provenance": {
    "created_at": "2026-09-01T10:55:35.175682+00:00",
    "code_commit": "996ac89dd9bb16d0ac38be27d53639bc9d4ff859",
    "python": "3.12.9",
    "platform": "macOS-26.5.2-arm64-arm-64bit",
    "dataset_dir": "/Users/bytedance/Documents/git_daiwk/auto-research/data",
    "dataset_fingerprint": "e66c261317a7b3179720ef3e3f5f79d7a204b135d1f217e2911b153ad2ce50a0",
    "packages": {
      "auto-research": "0.1.0",
      "numpy": "2.5.2",
      "torch": "2.13.0",
      "transformers": "5.14.1"
    },
    "artifact_path": "docs/reproductions/2608.10182-incrementality/metrics/public-seeds42-44.json"
  },
  "evaluation_protocol": {
    "tier": "l2_public_dataset",
    "tier_label": "L2 公开数据集训练",
    "seeds": [
      42,
      43,
      44
    ],
    "budget": "220 users; 30% treatment budget",
    "formal_comparison": true,
    "claim_policy": "formal multi-seed comparison"
  },
  "seed_results": [
    {
      "paper": {
        "arxiv_id": "2608.10182",
        "title": "From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation",
        "url": "https://arxiv.org/abs/2608.10182",
        "organization": "LinkedIn"
      },
      "dataset": {
        "name": "MovieLens 100K causal targeting simulation",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "incrementality",
        "same_budget_and_population": true
      },
      "baseline": {
        "name": "predictive targeting by estimated treated outcome",
        "policy_value": 0.09536825635811441,
        "total_incremental_value": 6.294304919635551,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.2307013774163521
      },
      "method": {
        "name": "uplift + uncertainty with global budget",
        "policy_value": 0.10407967501332929,
        "total_incremental_value": 6.869258550879733,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.5715856075559105
      },
      "relative": {
        "policy_value_percent": 9.134505534528072
      },
      "stages": {
        "examples": 220,
        "allocated_users": 66,
        "mean_propensity": 0.433110027582525,
        "treatment_rate": 0.41818181818181815,
        "uplift_rmse": 0.01841951085332956
      },
      "paper_results": {
        "linkedin_long_term_value_lift_percent": 7.2,
        "p_value": 0.041
      },
      "scope": "实际执行有混杂的 treatment 日志构造、双 outcome 回归、个体 uplift、不确定性探索分数和固定预算全局分配；未复刻 LinkedIn Transformer/DragonNet、在线神经 bandit 服务和大规模 LP 基础设施。"
    },
    {
      "paper": {
        "arxiv_id": "2608.10182",
        "title": "From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation",
        "url": "https://arxiv.org/abs/2608.10182",
        "organization": "LinkedIn"
      },
      "dataset": {
        "name": "MovieLens 100K causal targeting simulation",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "incrementality",
        "same_budget_and_population": true
      },
      "baseline": {
        "name": "predictive targeting by estimated treated outcome",
        "policy_value": 0.10420952201877175,
        "total_incremental_value": 6.877828453238935,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.6337659685926004
      },
      "method": {
        "name": "uplift + uncertainty with global budget",
        "policy_value": 0.10650250149155502,
        "total_incremental_value": 7.029165098442631,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.7133776592203113
      },
      "relative": {
        "policy_value_percent": 2.2003550427668466
      },
      "stages": {
        "examples": 220,
        "allocated_users": 66,
        "mean_propensity": 0.433110027582525,
        "treatment_rate": 0.4409090909090909,
        "uplift_rmse": 0.01619677735673771
      },
      "paper_results": {
        "linkedin_long_term_value_lift_percent": 7.2,
        "p_value": 0.041
      },
      "scope": "实际执行有混杂的 treatment 日志构造、双 outcome 回归、个体 uplift、不确定性探索分数和固定预算全局分配；未复刻 LinkedIn Transformer/DragonNet、在线神经 bandit 服务和大规模 LP 基础设施。"
    },
    {
      "paper": {
        "arxiv_id": "2608.10182",
        "title": "From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation",
        "url": "https://arxiv.org/abs/2608.10182",
        "organization": "LinkedIn"
      },
      "dataset": {
        "name": "MovieLens 100K causal targeting simulation",
        "users": 220,
        "items": 360
      },
      "setup": {
        "adapter": "incrementality",
        "same_budget_and_population": true
      },
      "baseline": {
        "name": "predictive targeting by estimated treated outcome",
        "policy_value": 0.10630232569677033,
        "total_incremental_value": 7.0159534959868415,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.6588439880466914
      },
      "method": {
        "name": "uplift + uncertainty with global budget",
        "policy_value": 0.10536922950608434,
        "total_incremental_value": 6.954369147401566,
        "budget_fraction": 0.3,
        "uplift_rank_correlation": 0.6153924361024425
      },
      "relative": {
        "policy_value_percent": -0.8777758949015529
      },
      "stages": {
        "examples": 220,
        "allocated_users": 66,
        "mean_propensity": 0.433110027582525,
        "treatment_rate": 0.4636363636363636,
        "uplift_rmse": 0.025474443672238115
      },
      "paper_results": {
        "linkedin_long_term_value_lift_percent": 7.2,
        "p_value": 0.041
      },
      "scope": "实际执行有混杂的 treatment 日志构造、双 outcome 回归、个体 uplift、不确定性探索分数和固定预算全局分配；未复刻 LinkedIn Transformer/DragonNet、在线神经 bandit 服务和大规模 LP 基础设施。"
    }
  ],
  "aggregate_metrics": {
    "dataset.users": {
      "mean": 220.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "dataset.items": {
      "mean": 360.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.policy_value": {
      "mean": 0.10196003469121884,
      "std": 0.005803758523857598,
      "ci95": 0.0065675710302791086,
      "n": 3
    },
    "baseline.total_incremental_value": {
      "mean": 6.729362289620442,
      "std": 0.38304806257460144,
      "ci95": 0.4334596879984211,
      "n": 3
    },
    "baseline.budget_fraction": {
      "mean": 0.3,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "baseline.uplift_rank_correlation": {
      "mean": 0.5077704446852146,
      "std": 0.24027625307729236,
      "ci95": 0.2718981764123368,
      "n": 3
    },
    "method.policy_value": {
      "mean": 0.10531713533698955,
      "std": 0.001212253021335965,
      "ci95": 0.001371793432060743,
      "n": 3
    },
    "method.total_incremental_value": {
      "mean": 6.95093093224131,
      "std": 0.08000869940817353,
      "ci95": 0.09053836651600884,
      "n": 3
    },
    "method.budget_fraction": {
      "mean": 0.3,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "method.uplift_rank_correlation": {
      "mean": 0.6334519009595548,
      "std": 0.07260065207691993,
      "ci95": 0.08215537179910609,
      "n": 3
    },
    "relative.policy_value_percent": {
      "mean": 3.485694894131122,
      "std": 5.128403138956043,
      "ci95": 5.803334468266818,
      "n": 3
    },
    "stages.examples": {
      "mean": 220.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.allocated_users": {
      "mean": 66.0,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.mean_propensity": {
      "mean": 0.433110027582525,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "stages.treatment_rate": {
      "mean": 0.4409090909090909,
      "std": 0.022727272727272735,
      "ci95": 0.02571833017299243,
      "n": 3
    },
    "stages.uplift_rmse": {
      "mean": 0.020030243960768464,
      "std": 0.004844029203358764,
      "ci95": 0.005481535066462318,
      "n": 3
    },
    "paper_results.linkedin_long_term_value_lift_percent": {
      "mean": 7.2,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    },
    "paper_results.p_value": {
      "mean": 0.041,
      "std": 0.0,
      "ci95": 0.0,
      "n": 3
    }
  },
  "manifest_ref": "reproduction:incrementality"
}