Realization of Causal Representation Learning and Redefined DAG for Causal AI

Causal DAG(Directed Acyclic Graph) usually lies in a 2D plane without distinguishing correlation changes and causal effects. Also, the causal effect is often approximately estimated by averaging the population's correlation changes. Now, AI(Artificial Intelligence) enables much larger-scale structural modeling, whose complex hidden confoundings make the approximation errors no longer ignorable but can snowball to considerable population-level Causal Representation Bias. Such bias has caused significant problems: ungeneralizable causal models, unrevealed individual-level features, not utilizable causal knowledge in DL(Deep Learning), etc. In short, DAG must be redefined to enable a new framework for causal AI. Observational time series can only reflect correlation changes in statistics. But the DL-based autoencoder can represent them as individual-level feature changes in latent space to reflect causal effects. In this paper, we introduce the redefined do-DAG concept and propose Causal Representation Learning (CRL) framework as the generic solution, along with a novel architecture to realize CRL and experimentally verify its feasibility.