论文标题
使用机器学习和气候应用估算面板数据中的连续治疗效果
Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application
论文作者
论文摘要
本文介绍并证明并证明了面板数据中连续治疗效应的新半参数估计量的渐近正态性。具体而言，我们估计平均导数。我们的估计器使用数据的面板结构来解释不可观察的时间不变的异质性和机器学习（ML）方法，以在建模高维关系的同时保持统计能力。我们使用双重偏偏机学习（DML）文献中的工具构建估计器。非线性面板设置中的蒙特卡洛模拟表明，相对于其他方法，我们的方法估计具有低偏差和方差的平均导数。最后，在灵活控制降水和其他天气特征之后，我们使用估算器来测量极热对美国（美国）玉米产量的影响。我们的方法产生的极端热效应估计值比使用线性回归大50％。估计值的这种差异对应于中位气候场景下的31.7亿美元的年度损害赔偿。我们还估计了剂量反应曲线，该曲线表明，在更极端热量暴露的县中，极端热量的损害有所下降。
This paper introduces and proves asymptotic normality for a new semi-parametric estimator of continuous treatment effects in panel data. Specifically, we estimate the average derivative. Our estimator uses the panel structure of data to account for unobservable time-invariant heterogeneity and machine learning (ML) methods to preserve statistical power while modeling high-dimensional relationships. We construct our estimator using tools from double de-biased machine learning (DML) literature. Monte Carlo simulations in a nonlinear panel setting show that our method estimates the average derivative with low bias and variance relative to other approaches. Lastly, we use our estimator to measure the impact of extreme heat on United States (U.S.) corn production, after flexibly controlling for precipitation and other weather features. Our approach yields extreme heat effect estimates that are 50% larger than estimates using linear regression. This difference in estimates corresponds to an additional $3.17 billion in annual damages by 2050 under median climate scenarios. We also estimate a dose-response curve, which shows that damages from extreme heat decline somewhat in counties with more extreme heat exposure.
