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

• URL: http://arxiv.org/abs/2211.08573v9
• Date: Fri, 22 Sep 2023 23:57:55 GMT
• Title: Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
• Authors: Jia Li, Xiang Li, Xiaowei Jia, Michael Steinbach, Vipin Kumar
• Abstract summary: Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane. In this paper, we redefine causal DAG as emphdo-DAG, in which variables' values are no longer time-stamp-dependent, and timelines can be seen as axes.
• Score: 28.133104562449212
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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