mlsanityData to 13 Oct 2026

Probabilistic Modelling is Sufficient for Causal Inference

arXiv 2512.23408 ↗First posted 29 Dec 2025stat.ML cs.LG stat.MEcited by 2

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Abstract (arXiv)

Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we want to make it clear that you can answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 24 of its 59 references that are in the map. First posted on arXiv 29 Dec 2025.

Builds on

24 of 59 references.

References · 24

  1. 2015Variational Inference with Normalizing Flowscited by 2,163
  2. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 934
  3. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 409
  4. 2021Self-Supervised Learning with Data Augmentations Provably Isolates Content from Stylecited by 309
  5. 2018Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learningcited by 279
  6. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  7. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  8. 2020Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effectscited by 72
  9. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  10. 2023Identifiability of latent-variable and structural-equation models: from linear to nonlinearcited by 58
  11. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  12. 2020Causal inference methods for combining randomized trials and observational studies: a reviewcited by 51
12 more
  1. 2023Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learningcited by 48
  2. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  3. 2020Probabilistic Reasoning across the Causal Hierarchycited by 19
  4. 2024Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learningcited by 16
  5. 2022Deep Structural Causal Shape Modelscited by 11
  6. 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractionscited by 11
  7. 2021Is Causal Reasoning Harder than Probabilistic Reasoning?cited by 10
  8. 2023A Measure-Theoretic Axiomatisation of Causalitycited by 8
  9. 2014Statistical Causality from a Decision-Theoretic Perspectivecited by 7
  10. 2023Dataflow graphs as complete causal graphscited by 5
  11. 2014Gaussian Process Structural Equation Models with Latent Variablescited by 3
  12. 2022Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomescited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world modelscited by 7
  2. 2026C-kNN-LSH: A Nearest-Neighbor Algorithm for Sequential Counterfactual Inferencecited by 0