  Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
Edges indicate causal effects' directions and imply their corresponding
time-passings. Due to the natural restriction of statistical models, effect
estimation is usually approximated by averaging the individuals' correlations,
i.e., observational changes over a specific time. However, in the context of
Machine Learning on large-scale questions with complex DAGs, such slight biases
can snowball to distort global models - More importantly, it has practically
impeded the development of AI, for instance, the weak generalizability of
causal models. In this paper, we redefine causal DAG as \emph{do-DAG}, in which
variables' values are no longer time-stamp-dependent, and timelines can be seen
as axes. By geometric explanation of multi-dimensional do-DAG, we identify the
\emph{Causal Representation Bias} and its necessary factors, differentiated
from common confounding biases. Accordingly, a DL(Deep Learning)-based
framework will be proposed as the general solution, along with a realization
method and experiments to verify its feasibility.

Machine Learning;Methodology