Recursive Causal Discovery

arXiv:2403.09300v1 Announce Type: new
Abstract: Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is daunting. This paper builds upon and extends four of our prior publications (Mokhtarian et al., 2021; Akbari et al., 2021; Mokhtarian …

abstract arxiv causal challenges complexity computational cs.lg data discovery domains effects errors graph identification key recursive results statistical stat.ml testing type