Environmental Justice Implications of Power Plant Emissions Control Policies: Heterogeneous Causal Effect Estimation under Bipartite Network Interference
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AI 根据所提供的论文材料归类：摘要明确研究电厂与受影响人群之间的二部网络干扰，并估计政策的子群因果效应，因此对应因果推断与网络干扰。
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电厂排放控制政策的环境正义影响：二部网络干扰下的异质因果效应估计
- 研究问题
- 当政策作用于电厂、健康结果出现在可能远离电厂的人群中时，如何估计不同社区的受益差异，并处理污染传播产生的二部网络干扰？
- 方法与思路
- 作者提出二部网络干扰下的子群因果效应估计量及经验蒙特卡洛评估方法，再结合子群发现方法，应用于美国 Medicare 受益人的健康数据。
- 材料陈述的贡献
- 摘要称，总体效应未达到统计显著，但高贫困率和高吸烟率社区的缺血性心脏病住院显著下降；方法还支持事先未指定的受益子群发现。
- 阅读关注点
- 应核对电厂与人群之间的网络构建、干预及暴露定义、因果识别条件，以及子群发现后的推断是否处理数据驱动选择与多重比较。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明识别假设、效应大小及子群推断细节，显著性也不等于政策效果已获验证。
原文摘要
Emissions generators, such as coal-fired power plants, are key contributors to air pollution and thus environmental policies to reduce their emissions have been proposed. Furthermore, marginalized groups are exposed to disproportionately high levels of this pollution and have heightened susceptibility to its adverse health impacts. As a result, robust evaluations of the heterogeneous impacts of air pollution regulations are key to justifying and designing maximally protective interventions. However, such evaluations are complicated in that much of air pollution regulatory policy intervenes on large emissions generators while resulting impacts are measured in potentially distant populations. Such a scenario can be described as that of bipartite network interference (BNI). To our knowledge, no literature to date has considered estimation of heterogeneous causal effects with BNI. In this paper, we contribute to the literature in a three-fold manner. First, we propose BNI-specific estimators for subgroup-specific causal effects and design an empirical Monte Carlo simulation approach for BNI to evaluate their performance. Second, we demonstrate how these estimators can be combined with subgroup discovery approaches to identify subgroups benefiting most from air pollution policies without a priori specification. Finally, we apply the proposed methods to estimate the effects of coal-fired power plant emissions control interventions on ischemic heart disease (IHD) among 27,312,190 US Medicare beneficiaries. Though we find no statistically significant effect of the interventions in the full population, we do find significant IHD hospitalization decreases in communities with high poverty and smoking rates.