A Generative Adversarial Framework for Bounding Confounded Causal Effects
A Generative Adversarial Framework for Bounding Confounded Causal Effects
复制标题
DOI：
10.1609/aaai.v35i13.17437
复制
发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Yaowei Hu;Yongkai Wu;Lu Zhang;Xintao Wu
中科院分区：
文献类型：
--
作者：
Yaowei Hu;Yongkai Wu;Lu Zhang;Xintao Wu
从观测数据中进行因果推理在许多领域得到了广泛的应用。然而，无法识别的情况下，因果关系的影响，不能唯一地计算从观测数据，造成严重的障碍，将因果推理复杂的真实的应用。在本文中，我们开发了一个边界的方法来估计平均因果效应（ACE）下无法识别的情况下，由于隐藏的混杂的基础上珍珠的结构因果模型。我们建议使用神经网络和隐式生成模型来参数化未知的外生随机变量和因果模型的结构方程。然后，使用对抗性学习框架，我们搜索参数空间以显式遍历与给定观测分布一致的因果模型，并找到那些最小化或最大化ACE的模型，以获得其上下界。该方法不对结构方程和变量的类型作任何假设。使用合成和真实世界的数据集进行实验。
Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl's structural causal model. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, using an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution, and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make assumption about the type of structural equations and variables. Experiments using both synthetic and real-world datasets are conducted.