Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we use double machine learning (DML) (Chernozhukov et al., 2018) to approximate high-dimensional and non-linear nuisance functions of the confounders to make inferences about the effects of policy interventions from panel data. We propose new estimators by adapting correlated random effects, within-group and first-difference estimation for linear models to an extension of Robinson (1988)'s partially linear regression model to static panel data models with individual fixed effects and unspecified non-linear confounder effects. Using Monte Carlo simulations, we compare the relative performance of different machine learning algorithms and find that conventional least squares estimators performs well when the data generating process is mildly non-linear and smooth, but there are substantial performance gains with DML in terms of bias reduction when the true effect of the regressors is non-linear and discontinuous. However, inference based on individual learners can lead to badly biased inference. Finally, we provide an illustrative example of DML for observational panel data showing the impact of the introduction of the minimum wage on voting behavior in the UK.

翻译：因果推断的最新进展催生了利用机器学习算法预测能力的方法开发。本文采用双机器学习方法（DML, Chernozhukov et al., 2018）对高维非线性混淆函数进行逼近，从而基于面板数据推断政策干预效应。通过将线性模型中的相关随机效应、组内估计和一阶差分估计方法拓展至Robinson（1988）部分线性回归模型的延伸形式，我们提出了适用于含个体固定效应及未指定非线性混淆效应的静态面板数据模型的新估计量。蒙特卡洛模拟显示：当数据生成过程呈现轻度非线性且光滑特征时，传统最小二乘估计量表现良好；但当回归变量真实效应为非线性且不连续时，DML方法在降低偏差方面具有显著优势。然而，基于单一学习器的推断可能导致严重的有偏推断。最后，我们通过观测面板数据实例展示了DML方法的应用——分析英国最低工资制度对投票行为的影响。