论文标题
使用因果关系正常化流量的社会科学中个性化公共政策分析
Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows
论文作者
论文摘要
结构方程/因果模型（SEMS/SCM）在流行病学和社会科学中广泛使用，以识别和分析平均因果效应（ACE）和条件ACE（CACE）。传统的因果效应估计方法，例如逆概率加权（IPW）和最近的回归 - 残基回归（RWR），因为它们避免了识别SCM参数的具有挑战性的任务，以估算ACE和CACE。但是，在传统估算方法可以用于反事实推理之前，还有许多工作要做，并为个性化的公共政策分析（p $^3 $ a）在社会科学中受益。尽管医生依靠个性化医学来在实验室环境（相对封闭的系统）中为患者量身定制治疗，但p $^3 $ a从这种裁缝中汲取灵感，但可以适应开放的社交系统。在本文中，我们开发了一种反事实推断的方法，即我们命名了因果 - 图标准化流量（C-GNF），促进p $^3 $ a。首先，我们展示了C-GNF如何捕获基础SCM而不对功能形式进行任何假设。其次，我们提出了一种新颖的取消化技巧来处理离散变量，这是对整个流量进行标准化的限制。第三，我们在实验中证明，C-GNF在估计ATE的偏差和差异方面与IPW和RWR进行了PAR，当真实的功能形式是已知的，并且当它们未知时更好。第四和最重要的是，我们对C-GNF进行了反事实推断，表明了有希望的经验表现。由于IPW和RWR与其他传统方法一样，缺乏反事实推断的能力，因此C-GNFS可能在调整个性化治疗方面发挥重要作用，促进P $^3 $ A并优化社会干预措施 - 与当前的“单型拟合”方法相反。
Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P$^3$A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P$^3$A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P$^3$A. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ATE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P$^3$A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.
