Deryugina Tatyana, Heutel Garth, Miller Nolan H, Molitor David, Reif Julian
Gies College of Business, University of Illinois, 340 Wohlers Hall, 1206 S. Sixth Street, Champaign, IL 61820.
Department of Economics, Georgia State University, PO Box 3992, Atlanta, GA 30302.
Am Econ Rev. 2019 Dec;109(12):4178-4219. doi: 10.1257/aer.20180279.
We estimate the causal effects of acute fine particulate matter exposure on mortality, health care use, and medical costs among the US elderly using Medicare data. We instrument for air pollution using changes in local wind direction and develop a new approach that uses machine learning to estimate the life-years lost due to pollution exposure. Finally, we characterize treatment effect heterogeneity using both life expectancy and generic machine learning inference. Both approaches find that mortality effects are concentrated in about 25 percent of the elderly population.
我们利用医疗保险数据估算了美国老年人急性细颗粒物暴露对死亡率、医疗保健使用情况和医疗成本的因果效应。我们利用当地风向变化来衡量空气污染,并开发了一种新方法,使用机器学习来估算因污染暴露而损失的生命年数。最后,我们使用预期寿命和通用机器学习推断来描述治疗效果的异质性。两种方法均发现,死亡效应集中在约25%的老年人群体中。