Targeted Learning Estimation of Sampling Variance for Improved Inference
以目标学习估计抽样方差以改进推断AI 解读 ↓
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以目标学习估计抽样方差以改进推断
- 研究问题
- 在小样本或接近违反正值性条件时，如何改进因果风险比估计的方差估计，缓解覆盖不足和第一类错误偏高？
- 方法与思路
- 作者针对对数因果风险比影响函数的方差推导有效影响函数，再通过一维通用最不利参数子模型构造一步目标方差估计。
- 材料陈述的贡献
- 摘要称，模拟中该方法在小样本及接近正值性违背时改善推断，使覆盖率更接近名义水平并降低第一类错误率。
- 阅读关注点
- 应核对方差目标与效应估计量的对应关系、更新步骤和所需条件，并查看正值性程度、比较方法及模拟误差的设定。
- 阅读边界
- 这是预印本，同行评审及期刊接收未获核实；摘要未给出完整识别与渐近条件，模拟表现不能保证所有参数或场景均改善。
原文摘要
For robust statistical inference it is crucial to obtain a good estimator of the variance of the proposed estimator of the statistical estimand. A commonly used estimator of the variance for an asymptotically linear estimator is the sample variance of the estimated influence function. This estimator has been shown to be anti-conservative in limited samples or in the presence of near-positivity violations, leading to elevated Type-I error rates and poor coverage. In this paper, capitalizing on earlier attempts at targeted variance estimators, we propose a one-step targeted variance estimator for the causal risk ratio (CRR) in scenarios involving treatment, outcome, and baseline covariates. While our primary focus is on the variance of log(CRR), our methodology can be extended to other causal effect parameters. Specifically, we focus on the variance of the IF for the log relative risk (log(CRR)) estimator, which requires deriving the efficient influence function for the variance of the IF as the basis for constructing the estimator. Several methods are available to develop efficient estimators of asymptotically linear parameters. In this paper, we concentrate on the so-called one-step targeted maximum likelihood estimator, which is a substitution estimator that utilizes a one-dimensional universal least favorable parametric submodel when updating the distribution. We conduct simulations with different effect sizes, sample sizes and levels of positivity to compare the estimator with existing methods in terms of coverage and Type-I error. Simulation results demonstrate that, especially with small samples and near-positivity violations, the proposed variance estimator offers improved performance, achieving coverage closer to the nominal level of 0.95 and a lower Type-I error rate.