mlsanityData to 7 Oct 2026

Differentiable Causal Discovery Under Latent Interventions

arXiv 2203.02336 ↗First posted 4 Mar 2022cs.LG stat.MEcited by 21

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Abstract (arXiv)

Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples and interventions is known, which is often unrealistic. We envision a scenario with an extensive dataset sampled from multiple intervention distributions and one observation distribution, but where we do not know which distribution originated each sample and how the intervention affected the system, textiti.e., interventions are entirely latent. We propose a method based on neural networks and variational inference that addresses this scenario by framing it as learning a shared causal graph among an infinite mixture (under a Dirichlet process prior) of intervention structural causal models. Experiments with synthetic and real data show that our approach and its semi-supervised variant are able to discover causal relations in this challenging scenario.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 4 Mar 2022.

Builds on

25 of 49 references.

References · 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 7; 2025: 5; 2026: 1.

21 papers.

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  1. 2023BISCUIT: Causal Representation Learning from Binary Interactionscited by 38
  2. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
  3. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  4. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  5. 2023Passive learning of active causal strategies in agents and language modelscited by 21
  6. 2022Causal Entropy Optimizationcited by 13
  7. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  8. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
  9. 2024ProDAG: Projected Variational Inference for Directed Acyclic Graphscited by 4
  10. 2025Curious Causality-Seeking Agents Learn Meta Causal Worldcited by 4
  11. 2024CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Seriescited by 3
  12. 2024psiDAG: Projected Stochastic Approximation Iteration for DAG Structure Learningcited by 3
9 more
  1. 2023Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Modelcited by 1
  2. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 1
  3. 2024Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyondcited by 1
  4. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  5. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 1
  6. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 1
  7. 2024Disentangled Representations for Causal Cognitioncited by 0
  8. 2025Theoretical Guarantees for Causal Discovery on Large Random Graphscited by 0
  9. 2026Efficient Differentiable Causal Discovery via Reliable Super-Structure Learningcited by 0