The Difference Between Causal Analysis and Predictive Models: Response to “Comment on Young and Holsteen (2017)”
The Difference Between Causal Analysis and Predictive Models: Response to “Comment on Young and Holsteen (2017)”
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因果分析和预测模型之间的区别：对“Young 和 Holsteen (2017) 评论”的回应
DOI：
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发表时间：
2018
影响因子：
6.3
通讯作者：
Cristobal Young
中科院分区：
文献类型：
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作者：
Cristobal Young
评论者的建议可能是一种合理的方法来解决预测建模中的不确定性，其中的目标是预测y。在治疗效果框架中，目标是通过观察到的条件来推断因果关系，评论者的建议存在严重缺陷。该建议(1)忽略了忽略变量偏差的定义，从而系统地省略了关键类型的控制；(2)为方便起见，假设模型空间中没有糟糕的控制，从而摒弃了模型不确定性的前提；(3)删除了几乎所有可选模型，以选择R2最高的单个模型。该建议不是显示需要什么模型假设来支持一个人的首选结果，而是倾向于有偏参数估计，并在任何人有机会看到替代结果之前删除它们。在治疗效应框架中，这不是模型稳健性分析，而是简单的有偏见的模型选择。
The commenter’s proposal may be a reasonable method for addressing uncertainty in predictive modeling, where the goal is to predict y. In a treatment effects framework, where the goal is causal inference by conditioning-on-observables, the commenter’s proposal is deeply flawed. The proposal (1) ignores the definition of omitted-variable bias, thus systematically omitting critical kinds of controls; (2) assumes for convenience there are no bad controls in the model space, thus waving off the premise of model uncertainty; and (3) deletes virtually all alternative models to select a single model with the highest R 2. Rather than showing what model assumptions are necessary to support one’s preferred results, this proposal favors biased parameter estimates and deletes alternative results before anyone has a chance to see them. In a treatment effects framework, this is not model robustness analysis but simply biased model selection.
影响因子：
10.5
作者：
Elwert F;Winship C
通讯作者：
Winship C
影响因子：
7.7
作者：
Cole, Stephen R.;Platt, Robert W.;Poole, Charles
通讯作者：
Poole, Charles