Deep Learning With DAGs
结合有向无环图的深度学习AI 解读 ↓
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结合有向无环图的深度学习
- 研究问题
- 给定研究者提出的因果有向无环图，如何减少任意函数形式限制，灵活估计图中可识别的总效应及其他因果效应？
- 方法与思路
- 作者提出因果图形归一化流，使用深度神经网络按给定 DAG 建模联合分布，再估计该图能够识别的因果目标。
- 材料陈述的贡献
- 摘要称，该方法支持多类可识别因果效应的灵活估计，并提供地位获得和社会流动模型再分析、开源软件及教程。
- 阅读关注点
- 应核对 DAG 的识别前提、归一化流如何遵守图结构、效应计算方式，以及模型逼近误差和估计不确定性如何处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明图设定错误或隐藏混杂下的保障，灵活建模仍依赖识别前提。
原文摘要
Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan's (1967) model of status attainment and Zhou's (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.