Automatic Debiased Machine Learning of Causal and Structural Effects
Automatic Debiased Machine Learning of Causal and Structural Effects
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DOI：
10.3982/ecta18515
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发表时间：
2022-05-01
期刊：
影响因子：
6.1
通讯作者：
Singh, Rahul
中科院分区：
文献类型：
--
作者：
Chernozhukov, Victor;Newey, Whitney K.;Singh, Rahul
许多因果效应和结构性效应依赖于回归。例子包括政策效应、平均衍生、回归分解、平均处理效应、因果中介和经济结构模型的参数。回归可能是高维的，这使得机器学习很有用。由于正则化和/或模型选择的偏差，将机器学习者插入识别方程可能导致较差的推断。本文给出了线性和非线性回归函数的自动去偏方法。在使用Lasso和兴趣函数时，消除偏差是自动的，而不需要完整的偏差校正。除偏可以应用于任何回归学习器，包括神经网络、随机森林、Lasso、boosting和其他高维方法。除了提供偏差校正外，我们还给出了对错误规范具有鲁棒性的标准误差，偏差校正的收敛速率，以及各种结构和因果效应估计量的渐近推断的原始条件。自动去偏见机器学习用于估计新南威尔士州职业培训数据对被治疗者的平均治疗效果，并从尼尔森扫描仪数据中估计需求弹性，同时允许偏好与价格和收入相关。
Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high-dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high-dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.