Connecting Data to Mechanisms with Meta Structural Causal Model
Heyang Gong · 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) · 2022
Recent years have seen impressive progress in theoretical and algorithmic developments of causal inference across various disciplines in science and engineering. However, there are still some unresolved theoretical problems, especially for cyclic causal relationships. In this article, we propose a meta structure causal model(meta-SCM) framework inspired by understanding causality as information transfer. A key feature of our framework is the introduction of the concept of active mechanisms to connect data and the collection of underlying causal mechanisms. We show that the meta-SCM provides a novel approach to address the theoretical complications for modeling cyclic causal relations. In addition, we propose a sufficient activated mechanisms assumption, and explain its relationship with existing hypotheses in causal inference and learning. Finally, we conclude the main idea of the meta-SCM framework with an emphasis on its theoretical and conceptual novelty.