arXiv:2504. 12594v2 Announce Type: replace Abstract: Conditional independence testing is a critical component of feature screening, invariant statistical models, and causal discovery.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2512. 19510v2 Announce Type: replace Abstract: Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions.
arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.
arXiv:2608. 11156v1 Announce Type: cross Abstract: Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions.
arXiv:2602. 01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process.
arXiv:2601. 02322v2 Announce Type: replace-cross Abstract: A common approach to out-of-distribution prediction restricts models to causal or invariant covariates to avoid spurious associations that may change across environments.
arXiv:2606. 00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess.