FiP: a Fixed-Point Approach for Causal Generative Modeling

arXiv:2404.06969v1 Announce Type: new
Abstract: Modeling true world data-generating processes lies at the heart of empirical science. Structural Causal Models (SCMs) and their associated Directed Acyclic Graphs (DAGs) provide an increasingly popular answer to such problems by defining the causal generative process that transforms random noise into observations. However, learning them from observational data poses an ill-posed and NP-hard inverse problem in general. In this work, we propose a new and equivalent formalism that do not require DAGs to describe …

abstract arxiv causal cs.lg data fixed-point generative generative modeling graphs however lies modeling noise popular process processes random science stat.ml them true type world