DAG(Directed Acyclic Graph) from causal inference does not differentiate causal effects and correlated changes. And the general effect of a population is usually approximated by averaging correlations over all individuals. Since AI(Artificial Intelligence) enables large-scale structure modeling on big data, the complex hidden confoundings have made these approximation errors no longer ignorable but snowballed to considerable modeling bias - Such Causal Representation Bias (CRB) leads to many problems: ungeneralizable causal models, unrevealed individual-level features, hardly utilized causal knowledge in DL(Deep Learning), etc. In short, DAG must be redefined to enable a new framework for causal AI. The observational time series in statistics can only represent correlated changes, while the DL-based autoencoder can represent them as individualized feature changes in latent space to estimate the causal effects directly. In this paper, we introduce the redefined do-DAG to visualize CRB, propose a generic solution Causal Representation Learning (CRL) framework, along with a novel architecture for its realization, and experimentally verify the feasibility.

翻译：因果推断中的有向无环图（DAG）无法区分因果效应与关联性变化，且通常通过取所有个体关联性的平均值来近似总体的一般效应。由于人工智能（AI）支持对大数据进行大规模结构建模，复杂的隐式混杂因素已使这些近似误差不再可忽略，反而滚雪球般累积为显著的建模偏差——这种因果表示偏差（CRB）导致诸多问题：不可泛化的因果模型、未被揭示的个体级特征、深度学习（DL）中难以应用的因果知识等。简言之，必须重新定义DAG以构建因果AI的新框架。统计学中的观测时间序列仅能表征关联性变化，而基于深度学习的自编码器可将其表示为隐空间中的个体化特征变化，从而直接估计因果效应。本文提出重定义的do-DAG以可视化CRB，构建通用解决方案——因果表示学习（CRL）框架及其实现的新型架构，并通过实验验证可行性。