Основні поняття
Causal Discovery with Single-parent Decoding (CDSD) is a novel method for learning causal representations from temporal data by leveraging a sparsity assumption (single-parent decoding) to achieve identifiability of both the latent representation and the causal graph over these latents.
Статистика
CDSD achieves a high MCC ≥0.95 in all linear settings.
Varimax-PCMCI requires over 24 hours for a single experiment with nonlinear dynamics and 1000 samples.
Varimax-PCMCI fails to recover the latent representation with nonlinear decoding, achieving poor MCC and SHD.
CDSD successfully clusters climate variables into geographically connected regions, unlike models without constraints.
Цитати
"The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies."
"In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable."
"A key innovation of this paper is that, with our sparse mapping assumption, we can identify the latents up to some benign indeterminacies (e.g., permutations) as well as the temporal causal graph over the latents."