Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space

TL;DR

This paper introduces a geometric approach to causal DAGs, redefining them as do-DAGs to address confounding bias in latent space, and proposes a deep learning framework to correct causal representation bias for improved AI generalizability.

Contribution

It redefines causal DAGs as do-DAGs independent of time-stamps, identifies causal representation bias, and develops a deep learning-based method to mitigate this bias in latent space.

Findings

The geometric interpretation of do-DAGs clarifies causal representation bias.

The proposed deep learning framework effectively reduces confounding bias.

Experiments demonstrate improved causal effect estimation accuracy.

Abstract

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.