Discovery

Causal discovery: LiNGAM identifiability, linear disentanglement, and invariant prediction tools for recovering structure from distributional and environment-shift information.

Invariant­Prediction 45 core · 28 supporting · 6 submodules Population identification of the direct causes of a target variable from the invariance of its conditional law across interventional environments, after Peters, Bühlmann & Meinshausen, *Causal inference using invariant p Linear­Disentanglement 89 core · 117 supporting · 11 submodules Identifiability of latent causal variables that follow a linear structural equation model and are observed only through an unknown full-rank linear mixing, from the precision matrices of one observational and several sin Li­NGAM 6 core · 0 supporting · 3 submodules Identifiability of the linear non-Gaussian acyclic model x = B·x + e (Shimizu et al.