mlsanityData to 14 Oct 2026

Predictive Coding beyond Correlations

arXiv 2306.15479 ↗First posted 27 Jun 2023cs.LGcited by 6

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Abstract (arXiv)

Recently, there has been extensive research on the capabilities of biologically plausible algorithms. In this work, we show how one of such algorithms, called predictive coding, is able to perform causal inference tasks. First, we show how a simple change in the inference process of predictive coding enables to compute interventions without the need to mutilate or redefine a causal graph. Then, we explore applications in cases where the graph is unknown, and has to be inferred from observational data. Empirically, we show how such findings can be used to improve the performance of predictive coding in image classification tasks, and conclude that such models are able to perform simple end-to-end causal inference tasks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

27 of 50 references.

References · 27

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  3. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  4. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
  5. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  6. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 211
  7. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 140
  8. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 140
  9. 2020Causal Autoregressive Flowscited by 99
  10. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 95
  11. 2022Deep End-to-end Causal Inferencecited by 95
  12. 2020Predictive Coding Approximates Backprop along Arbitrary Computation Graphscited by 90
15 more
  1. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  2. 2016Discovering Causal Signals in Imagescited by 79
  3. 2020The Neural Coding Framework for Learning Generative Modelscited by 44
  4. 2021Associative Memories via Predictive Codingcited by 44
  5. 2018Biologically Motivated Algorithms for Propagating Local Target Representationscited by 43
  6. 2022Learning on Arbitrary Graph Topologies via Predictive Codingcited by 33
  7. 2018Deep Predictive Coding Network with Local Recurrent Processing for Object Recognitioncited by 25
  8. 2023Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queriescited by 24
  9. 2021Reverse Differentiation via Predictive Codingcited by 23
  10. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  11. 2022Predictive Coding beyond Gaussian Distributionscited by 16
  12. 2019Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgettingcited by 15
  13. 2022BayesPCN: A Continually Learnable Predictive Coding Associative Memorycited by 14
  14. 2022Deep Counterfactual Estimation with Categorical Background Variablescited by 5
  15. 2022Robust Graph Representation Learning via Predictive Codingcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 0; 2026: 1.

6 papers.

  1. 2023Brain-inspired Computational Intelligence via Predictive Codingcited by 30
  2. 2024Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?cited by 10
  3. 2024Predictive Coding Networks and Inference Learning: Tutorial and Surveycited by 7
  4. 2024A Review of Neuroscience-Inspired Machine Learningcited by 6
  5. 2024Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagationcited by 4
  6. 2026A Study of Hidden-State Optimization Order in Predictive Coding Networkscited by 0