A Plasmode Benchmark Can Refute but Cannot Certify: Projection and Regularity in Causal-Inference Simulation
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半合成数据基准可反驳但不能认证：因果模拟中的投影与正则性
- 研究问题
- 从真实队列重采样协变量并重新生成结果的半合成模拟，能支持哪些估计器比较结论，其难度又由哪些设计选择决定？
- 方法与思路
- 作者区分重叠条件的保留与结果模型对匹配估计器的偏向，提出满足正则要求的模拟算法，并结合真实数据模拟分析。
- 材料陈述的贡献
- 摘要主张，此类基准可在满足保证条件时反驳普遍性断言，却不能认证目标总体表现；修复重叠也未必消除模型投影偏向。
- 阅读关注点
- 应核对模拟分布是否属于被比较理论的适用类、处理重生成和协变量粗化如何改变重叠，以及结果模型如何影响方法排序。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要中的覆盖失败与排序逆转有特定设计背景，不能泛化到所有方法。
原文摘要
Plasmode simulation - resampling covariates, and often treatment, from a real cohort and regenerating the outcome from a model with an injected effect - is a standard tool for comparing causal-inference estimators. We argue that its validity turns on two separable design choices, and that separating them clarifies a recent debate over resampling versus regenerating the treatment. Our central message limits what such a benchmark can show: a plasmode can refute a universal claim about an estimator but cannot certify one. Refuting needs only that the simulated data genuinely belong to the class over which a guarantee is quoted; certifying also needs the benchmark's difficulty to carry over to the target population, which the analyst cannot verify. Two choices set that difficulty. Regularity is whether the simulated law preserves the overlap that inverse-weighting and targeted-learning theory require. Resampling the real treatment with fine-grained covariates destroys it, and the resulting coverage failure falls on flexible, machine-learning-based estimators, while parametric-nuisance estimators - including inverse-probability weighting - stay near nominal; it disappears once the treatment is regenerated from a bounded propensity or the covariates are coarsened, and cross-fitting removes only part of it. Projection is that the injected outcome model hands a matching estimator the right answer by construction; this survives the regularity fix and can reverse estimator rankings even when overlap is intact. We give a regular-plasmode algorithm, a confirming real-data simulation, and practical recommendations. A plasmode is a filter against estimators, not a warrant for them.