arXiv:2608.23660v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be trusted remains unclear. We systematically evaluat…
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent and per-problem hyperparameter tuning of continuous-optimization methods are poorly suited to two prac…
arXiv stat.ML
TIER_1English(EN)·Jan Marco Ruiz de Vargas, Kirtan Padh, Niki Kilbertus·
arXiv:2512.10032v3 Announce Type: replace-cross Abstract: Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several chall…
arXiv stat.ML
TIER_1English(EN)·Avni Rajpal, Anubhav Kumar, Rishabh Karnad, Mohammad Emtiyaz Khan, P. K. Srijith·
arXiv:2608.22212v1 Announce Type: cross Abstract: Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate modeling, it is more meaningful to consider interactions among groups of variables.…
arXiv stat.ML
TIER_1English(EN)·Daniela Schkoda, Philipp Faller, Patrick Bl\"obaum, Dominik Janzing·
arXiv:2411.05625v2 Announce Type: replace Abstract: We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the causal model. Specifically, given data on $X, Y, \…
