Posts tagged Nonlinear data
Nonlinear dependence
- October 23, 2026
A constraint-based method learns a graph from independence tests. Its result depends on the test. The default test, partial correlation, detects only linear dependence. When an effect is nonlinear, it can miss an edge or add a false one. In 0.8.0, you can use PC, FCI, GFCI, and CD-NOD with four supported tests that detect nonlinear dependence: KCI, RCoT, CMIknn, and GPDC. Each runs faster than the package we measured it against at every size we timed, at the same level of accuracy: PC with RCoT runs more than 5x faster than causal-learn and more than 9x faster than pcalg.