Targeted Deep Survival Contrasts: Valid Inference for Treatment-Specific Survival Benefit with Neural Networks
靶向深度生存对比：神经网络下处理特定生存收益的有效推断AI 解读 ↓
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靶向深度生存对比：神经网络下处理特定生存收益的有效推断
- 研究问题
- 在观察数据存在混杂和依赖协变量的删失时，如何用神经网络估计处理特定生存曲线及其差异，并构造有效同时置信带？
- 方法与思路
- 作者在网络权重空间实施联合靶向更新，以影响函数残差作一步补正，并通过交叉拟合和乘子自助法开展联合推断。
- 材料陈述的贡献
- 摘要称，该方法建立联合渐近线性和置信带理论；残差补正具有双重稳健性，并在结果模型严重错误时恢复目标推断。
- 阅读关注点
- 核对投影工作参数与不受限因果目标的区别、删失和混杂的识别条件，以及补正、交叉拟合和置信带的成立要求。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；模拟数值仅限所述设定，摘要明确报告未补正版本在错设时可能失效。
原文摘要
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.