Pallavi Basuʼs Contribution to the Discussion of ‘Assumption-Lean Inference for Generalised Linear Model Parameters’ by Vansteelandt and Dukes
Pallavi Basu 对《广义线性模型参数的弱假设推断》的讨论AI 解读 ↓
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Pallavi Basu 对《广义线性模型参数的弱假设推断》的讨论
- 研究问题
- 这篇讨论关注弱假设推断中主效应与交互效应的解释、机器学习辅助估计的使用，以及如何明确因果识别与联合推断目标。
- 方法与思路
- 讨论者评议有效影响函数方法，提出交叉拟合、模型选择、同时区间及未测混杂敏感性分析等需要进一步考虑的问题。
- 材料陈述的贡献
- 原文提供的是对主论文的评论与建议，强调目标参数解释、联合检验和因果识别条件，未在材料中建立新的估计定理。
- 阅读关注点
- 应区分评论建议与主论文结果，核对效应目标的定义、机器学习误差条件、交互效应解释及因果识别假设的原始表述。
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原文摘要
The authors propose an ingenious way to improve on providing inference for the main and interaction effects for generalised linear model parameters. With the use of the efficient influence function, they can extend the allowable bias introduced by machine learning estimators up to op(n −1/4). Noting that cross-fitting has been suggested to counter the Donsker condition, it may be a useful alternative to contrast with machine learning estimates, averages over sample splits or even averages over several models. A minimiser of the Kullback–Leibler divergence style model selection procedure of the ‘best’ model within the class of considered models has the advantage of having a model available at disposal. This may be useful in explaining to applied collaborators or providing out-of-sample estimates or prediction intervals. The philosophy of addressing model misspecification used in this work loses that connection. It could be not very clear, a priori, how to choose between the main effect versus an interaction effect estimand. Suppose there is an interest in the main effect. However, it is expected for the linear model to have interaction terms; how would one interpret the main effect estimand in the interaction model? Although a little far-fetched, practitioners are often interested in testing the interaction effect of a group of covariates, say, the interactions of a policy and socioeconomic factors on health outcome; a model then aids in simpler joint statistical tests. Analogously, simultaneous intervals or confidence sets will be useful here. Furthermore, in the spirit of the recommended ‘hierarchical principle’, how would we interpret estimated effects when the confidence interval (CI) for the interaction estimand does not include the null and the CI for the main effect includes zero or no effect? Further, to connect the dots well, it will be great to understand the causal identifying conditions, considering that inference rather than prediction is the goal, an edge that will greatly help declutter the uses of black-box methodologies. Thereafter, connecting to unmeasured confounding either via Rosenbaum’s sensitivity or VanderWeele's E-value will be beneficial. However, the inferential nature of the causal estimand, either testing or providing valid confidence intervals, should remain.