We revisit a novel causal model published in Demography by Hicks et al. (2018), designed to assess whether exposure to neighborhood disadvantage over time affects children's reading and math skills. Here, we provide corrected and new results. Reconsideration of the model in the original article raised concerns about bias due to exposure-induced confounding (i.e., past exposures directly affecting future exposures) and true state dependence (i.e., past exposures affecting confounders of future exposures). Through simulation, we show that our originally proposed propensity function approach displays modest bias due to exposure-induced confounding but no bias from true state dependence. We suggest a correction based on residualized values and show that this new approach corrects for the observed bias. We contrast this revised method with other causal modeling approaches using simulation. Finally, we reproduce the substantive models from Hicks et al. (2018) using the new residuals-based adjustment procedure. With the correction, our findings are essentially identical to those reported originally. We end with some conclusions regarding approaches to causal modeling.

中文翻译：

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希克斯等人关于“连续邻域效应”的注释。（2018）。

我们重新审视希克斯等人在《人口统计学》上发表的新颖因果模型。（2018），旨在评估随着时间的推移，暴露在邻里劣势是否会影响儿童的阅读和数学技能。在这里，我们提供修正后的新结果。对原始文章中模型的重新考虑引起了对由于暴露引起的混杂因素（即过去的暴露直接影响未来暴露）和真实状态依赖性（即过去的暴露影响未来暴露的混杂因素）造成的偏差的担忧。通过模拟，我们表明，我们最初提出的倾向函数方法由于暴露引起的混杂而显示出适度的偏差，但没有来自真实状态依赖性的偏差。我们建议根据残差值进行修正，并表明这种新方法可以纠正观察到的偏差。我们将这种修改后的方法与使用模拟的其他因果建模方法进行了对比。最后，我们重现了 Hicks 等人的实质性模型。（2018）使用新的基于残差的调整程序。经过更正，我们的发现与最初报告的结果基本相同。最后我们得出一些关于因果建模方法的结论。