Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials
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通过营养干预试验的现实模拟评估目标最大似然估计的稳健性
- 研究问题
- 论文研究采用机器学习估计辅助函数时，平均处理效应估计及区间推断在接近真实研究复杂度的模拟中是否稳健，交叉验证有何作用。
- 方法与思路
- 作者基于十项营养干预研究，用欠平滑的高自适应套索拟合数据生成分布，再模拟并比较多类估计量、标准误和置信区间。
- 材料陈述的贡献
- 摘要建议使用替代估计与估计方程方法的交叉验证版本，作者认为额外的交叉验证有助于减少辅助函数过拟合并改善推断稳健性。
- 阅读关注点
- 阅读全文时应核对模拟分布如何保留真实数据复杂度、欠平滑规则与交叉验证设计，并比较偏差、标准误及区间覆盖的具体证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出各方法的量化表现，基于拟合分布的模拟也不能保证所有环境中的稳健性。
原文摘要
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across ``real-world'' simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A realistic set of simulations, based on a novel method, highly adaptive lasso, for estimating the data-generating distribution that guarantees a certain level of complexity (undersmoothing) is used to better mimic the complexity of the true data-generating distribution. We have applied this novel method for estimating the data-generating distribution by individual study and to subsequently use these fits to simulate data and estimate treatment effects parameters as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.