mlsanityData to 14 Oct 2026

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

arXiv 2303.12703 ↗First posted 22 Mar 2023cs.LG stat.MEcited by 15

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using ADMGs are based on either linear functional assumptions or a discrete search that is complicated to use and lacks computational tractability for large datasets. In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data. We first show that the presence of latent confounding is identifiable under the assumptions of bow-free ADMGs with non-linear additive noise models. With this insight, we propose a novel neural causal model based on autoregressive flows for ADMG learning. This not only enables us to determine complex causal structural relationships behind the data in the presence of latent confounding, but also estimate their functional relationships (hence treatment effects) simultaneously. We further validate our approach via experiments on both synthetic and real-world datasets, and demonstrate the competitive performance against relevant baselines.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 22 of its 51 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

22 of 51 references.

References · 22

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  3. 2019Variational Autoencoders and Nonlinear ICA: A Unifying Frameworkcited by 503
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  5. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  6. 2017Advances in Variational Inferencecited by 236
  7. 2019Gradient-Based Neural DAG Learningcited by 227
  8. 2019Learning Sparse Nonparametric DAGscited by 207
  9. 2018The Blessings of Multiple Causescited by 104
  10. 2022Deep End-to-end Causal Inferencecited by 95
  11. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  12. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
10 more
  1. 2013Learning Sparse Causal Models is not NP-hardcited by 49
  2. 2020Causal Discovery with Unobserved Confounding and non-Gaussian Datacited by 25
  3. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  4. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  5. 2018Multiple Causal Inference with Latent Confoundingcited by 14
  6. 2017Implicit Causal Models for Genome-wide Association Studiescited by 10
  7. 2021Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confoundingcited by 9
  8. 2015Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagramscited by 8
  9. 2021Causal Structural Learning Via Local Graphscited by 7
  10. 2019Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal modelscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 1; 2025: 2; 2026: 6.

15 papers.

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  1. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  2. 2023Scalable Counterfactual Distribution Estimation in Multivariate Causal Modelscited by 6
  3. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  4. 2025Causal Additive Models with Unobserved Causal Paths and Backdoor Pathscited by 5
  5. 2023Neuro-Causal Factor Analysiscited by 4
  6. 2023Learned Causal Method Predictioncited by 3
  7. 2026FoundCause: Causal Discovery with Latent Confounders from Observational Datacited by 3
  8. 2023CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Predictioncited by 2
  9. 2025Intervening to Learn and Compose Causally Disentangled Representationscited by 1
  10. 2026Large Causal Models for Temporal Causal Discoverycited by 1
  11. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  12. 2026Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variablescited by 1
3 more
  1. 2023Amortised Inference in Bayesian Neural Networkscited by 0
  2. 2026Efficient Differentiable Causal Discovery via Reliable Super-Structure Learningcited by 0
  3. 2026How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confoundingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data
  2. 2026From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
  3. 2026From Causal Discovery to Dynamic Causal Inference in Neural Time Series
  4. 2026Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
  5. 2025Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery
  6. 2022Active Bayesian Causal Inference
  7. 2023Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
  8. 2026Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation