Causal-StoNet: Causal Inference for High-Dimensional Complex Data

arXiv:2403.18994v1 Announce Type: cross
Abstract: With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear. As a result, the task of making causal inference with high-dimensional complex data has become a fundamental problem in many disciplines, such as medicine, econometrics, and social science. However, the existing methods for causal inference are frequently developed …

abstract advancement arxiv become causal causal inference collection cs.lg data data science datasets inference making process science stat.ml type