论文标题
深度：半参数因果中介分析，具有认真的深度学习
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
论文作者
论文摘要
因果中介分析可以解开因果关系的黑匣子，因此是解开生物医学和社会科学中因果关系途径的强大工具，也是评估机器学习公平性的功能。为了减少在中介分析中估计自然直接和间接效应的偏见，我们提出了一种称为DeepMed的新方法，该方法使用深层神经网络（DNNS）在有效影响功能中跨越无限二维的滋扰功能。我们获得了新的理论结果，我们的深度方法（1）可以在不对DNN结构施加稀疏性约束的情况下达到半摩托效率，并且（2）可以适应扰动函数的某些低维度结构，从而在基于DNN的基于DNN的半疗法中的现有文献中显着提高了基于DNN的文献。进行了广泛的合成实验，以支持我们的发现，并揭示理论与实践之间的差距。作为概念证明，我们将深入分析机器学习公平性的两个真实数据集并得出与以前的发现一致的结论。
Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.
