论文标题
通过隐式生成模型的部分识别治疗效果
Partial Identification of Treatment Effects with Implicit Generative Models
论文作者
论文摘要
我们考虑了部分识别的问题，即观察数据对治疗效果的界限的估计。尽管使用离散治疗变量或在特定因果图（例如仪器变量）中进行了研究，但最近已经使用深层生成建模工具探索了部分识别。我们提出了一种使用包含连续和离散随机变量的隐式生成模型在一般因果图中部分鉴定平均治疗效应（ATE）的新方法。由于与连续治疗的食物通常不规则，因此我们利用响应函数的部分导数定义了ATE的常规近似值，这是我们称之为统一的平均治疗衍生物（UATD）的数量。我们证明，我们的算法在线性结构因果模型（SCM）中收敛到ATE的紧密界限。对于非线性SCM，我们从经验上表明，与直接优化ATE的方法相比，使用UATD会导致更紧密，更稳定的边界。
We consider the problem of partial identification, the estimation of bounds on the treatment effects from observational data. Although studied using discrete treatment variables or in specific causal graphs (e.g., instrumental variables), partial identification has been recently explored using tools from deep generative modeling. We propose a new method for partial identification of average treatment effects(ATEs) in general causal graphs using implicit generative models comprising continuous and discrete random variables. Since ATE with continuous treatment is generally non-regular, we leverage the partial derivatives of response functions to define a regular approximation of ATE, a quantity we call uniform average treatment derivative (UATD). We prove that our algorithm converges to tight bounds on ATE in linear structural causal models (SCMs). For nonlinear SCMs, we empirically show that using UATD leads to tighter and more stable bounds than methods that directly optimize the ATE.
