mlsanityData to 7 Oct 2026

Neuro-Causal Factor Analysis

arXiv 2305.19802 ↗First posted 31 May 2023stat.ML cs.LGcited by 4

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Abstract (arXiv)

Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is widely used in the psychological, biological, and physical sciences. We revisit this classic method from the perspective of recent advances in causal structure learning and deep generative models, introducing a framework for Neuro-Causal Factor Analysis (NCFA). Our approach is fully nonparametric: it learns a directed graph between latent and observed variables, and then fits a deep generative model constrained to respect the Markov factorization of the graph. Empirically, on synthetic and real data, NCFA attains better reconstruction error compared to standard FA and better latent distribution recovery compared to a standard variational autoencoder, all with the advantages of sparser architecture, lower model complexity, and causal interpretability.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 46 of its 121 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

46 of 121 references.

References · 46

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34 more
  1. 2022Weakly supervised causal representation learningcited by 140
  2. 2022Interventional Causal Representation Learningcited by 140
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  2. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
  3. 2025Intervening to Learn and Compose Causally Disentangled Representationscited by 1
  4. 2026Unsupervised Causal Abstractions Discoverycited by 0