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.

翻译：社会科学理论常涉及一组变量或事件之间的因果关联。尽管有向无环图（DAGs）日益被用于表征这些理论，但其全部潜力在实践中尚未得到充分实现。作为非参数化因果模型，DAGs无需对假设关系的函数形式进行预设。然而，为了简化实证评估任务，研究者仍倾向于引入此类假设，即便这些假设通常具有随意性，且无法反映任何理论内涵或先验知识。更关键的是，当函数形式假设无法准确捕捉所研究因果系统的复杂性时，这类假设可能引发偏差。本文提出因果图归一化流（cGNFs）这一创新的因果推断方法，通过深度神经网络对以DAGs表征的理论进行实证评估。与传统方法不同，cGNFs依据分析者提供的DAG建模数据的完整联合分布，无需依赖严格的函数形式假设。该方法能灵活地对所有可从DAG识别的因果估计量——包括总效应、条件效应、直接与间接效应以及路径特定效应——进行半参数估计。我们通过重分析Blau与Duncan（1967）的地位获得模型及Zhou（2019）的条件流动与受控流动模型来演示该方法。为促进应用，我们提供开源软件及系列在线教程以实施cGNFs。文章最后讨论了当前方法的局限性及未来发展方向。