论文标题
估计因果效应的元学习者：有限样本交叉拟合性能
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance
论文作者
论文摘要
使用机器学习方法对因果效应的估计已成为计量经济学的积极研究领域。在本文中，我们研究了元学习者的有限样本性能，以估算样品分解和交叉拟合的异质治疗效果，以减少过度拟合的偏见。在合成和半合成模拟中，我们发现有限样品中的元学习者的性能在很大程度上取决于估计程序。结果表明，样品分解和交叉拟合在大型样品中分别有益于偏移和元素的效率，而在小样本中则优选全样本估计。此外，我们根据特定的数据特征（例如治疗共享和样本量）提出了在经验研究中应用特定元学习者的实用建议。
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic simulations we find that the performance of the meta-learners in finite samples greatly depends on the estimation procedure. The results imply that sample-splitting and cross-fitting are beneficial in large samples for bias reduction and efficiency of the meta-learners, respectively, whereas full-sample estimation is preferable in small samples. Furthermore, we derive practical recommendations for application of specific meta-learners in empirical studies depending on particular data characteristics such as treatment shares and sample size.
