Causal graphs are usually considered in a 2D plane, but it has rarely been noticed that within multiple relatively independent timelines, which is comparatively common in causality machine learning, the individual-level differences may lead to Causal Representation Bias (CRB). More importantly, such a blind spot has brought obstacles to interdisciplinary applications. Deep Learning (DL) methods overlooking CRBs confront the trouble of models' generalizability, while statistical analytics face difficulties in modeling individual-level features without a geometric global view. In this paper, we initially discuss the Geometric Meaning of causal graphs regarding multi-dimensional timelines; and, accordingly, analyze the scheme of CRB and explicitly define causal model generalization and individualization from a geometric perspective. We also spearhead a novel framework, Causal Representation Learning (CRL), to construct a valid learning plane (in latent space) for causal graphs, propose a particular autoencoder architecture to realize it, and experimentally prove the feasibility. Involved causal data includes Electronic Healthcare Records (EHR) to estimate medical effects and a hydrology dataset to forecast the environmentally influenced streamflow.
翻译:2D平面通常考虑因果图,但很少注意到,在多个相对独立、相对比较常见的因果关系机器学习时间表中,个人层面的差异可能导致因果代表比亚斯(CRB),更重要的是,这种盲点给跨学科应用带来了障碍。 忽视CRB的深层学习(DL)方法面临模型通用性的难题,而统计分析方法在不从几何角度进行个人层面特征建模时面临困难。在本文中,我们最初讨论的是多维时间框架因果图表的几何含义;因此,我们分析了CRB的图案,并明确定义了因果模型的概括化和个性化。我们还率先提出了一个新的框架,即Cusal代表学习(CRL),为因果图表构建一个有效的学习平面(潜伏空间),提出实现该图的某种自动电解结构,并实验性地证明可行性。 参与的因果数据包括电子保健记录,以估计医疗影响和水文数据集,以预测环境影响的溪流。