AI 中文总结
该研究提出TDSC方法，结合TDA与乘数自助法，实现治疗特异性生存获益的有效推断，在模拟数据中表现出更低MSE与双重鲁棒性。
AI 中文摘要
神经生存模型越来越多地被要求支持反事实推断——即治疗会如何改变人群的生存情况，而非仅提供预后风险评分。从观察性数据回答这类问题，需要在存在混杂和协变量相关删失的情况下，对治疗特异性生存对比进行有效推断，而标准深度生存估计器在这些目标上存在偏差，且无法提供可靠的不确定性估计。我们提出靶向深度生存对比（TDSC），它将靶向深度架构（TDA，嵌入网络权重空间的靶向最大似然估计）扩展到时间网格上的完整治疗特异性生存曲线向量，进而扩展到获益曲线和限制平均生存时间（RMST）差值。一条通用的靶向路径——每次迭代将堆叠的有效影响函数投影到闭式最后一层梯度上的岭投影——同时求解所有坐标的投影估计方程；单步残差补充将插件估计转化为无约束目标的双重鲁棒估计；乘数自助法为获益曲线生成同时置信带。我们证明了交叉拟合变体的联合渐近线性性、带有效性和补充的双重鲁棒性，该变体无需Donsker条件。在7个存在混杂治疗、符号变化效应异质性和依赖删失的蒙特卡洛模拟库中，TDSC插件实现了名义逐点和同时覆盖，其均方误差（MSE）比基于相同干扰拟合构建的每时间点单步AIPCW低35%。在结果模型严重错误设定的情况下，插件跟踪其工作参数且区间失效（覆盖率44%），而补充则恢复了无约束因果目标的名义推断（覆盖率94%-95%），且样本内诊断可区分这两种情况。
英文摘要
Neural survival models are increasingly asked to support counterfactual claims---how much a treatment would change survival in a population---rather than only prognostic risk scores. Answering such questions from observational data requires valid inference for treatment-specific survival contrasts under confounding and covariate-dependent censoring, targets for which standard deep survival estimators are biased and provide no honest uncertainty. We propose Targeted Deep Survival Contrasts (TDSC), which extends Targeted Deep Architectures (TDA)---targeted maximum likelihood estimation embedded in a network's weight space---to the full vector of treatment-specific survival curves over a time grid, and hence to the benefit curve and the restricted mean survival time (RMST) difference. A single universal targeting path, one ridge projection of the stacked efficient influence functions onto closed-form last-layer gradients per iteration, simultaneously solves the projected estimating equations for all coordinates; a one-step residual top-up converts the plug-in into a doubly robust estimator of the unrestricted target; and a multiplier bootstrap yields simultaneous confidence bands for the benefit curve. We prove joint asymptotic linearity, band validity, and double robustness of the top-up for a cross-fitted variant requiring no Donsker conditions. Across seven Monte Carlo banks with confounded treatment, sign-varying effect heterogeneity, and dependent censoring, the TDSC plug-in attains nominal pointwise and simultaneous coverage with 35% lower MSE than a per-timepoint one-step (AIPCW) built from the same nuisance fits. Under a badly wrong outcome model the plug-in tracks its working parameter and its intervals fail (44% coverage), while the top-up restores nominal inference for the unrestricted causal target (94-95%)---and in-sample diagnostics separate the two regimes.
Comments19 pages, 4 figures, 4 tables
