Causal Representation Learning for Explainable Embodied Decision-Making with Interactive World Models
Keywords:
Causal representation learning; interactive world models; explainable AI; embodied decision-making; counterfactual reasoning; system architectureAbstract
Embodied decision-making systems operating in complex, dynamic environments require not only accurate predictive models but also the capacity to produce counterfactual explanations that engender human trust and enable robust policy adaptation. This paper presents a system-level inquiry into the integration of causal representation learning within interactive world models for explainable embodied agents. We argue that purely associative world models, despite their predictive strength, lack the structural constraints necessary to support intervention and counterfactual reasoning, which are foundational for transparency, fairness, and safe deployment. By recasting world model learning through a causal lens, representations can be structured around independent causal mechanisms, permitting agents to simulate consequences of alternative actions and to communicate decision rationales in terms of cause-effect relationships. We examine the architectural trade-offs involved in coupling action-aware interactive world models with causal representation modules, analyzing the tensions between model capacity, real-time inference, data efficiency, and explanatory fidelity. The paper further explores governance and policy implications arising from such systems, including counterfactual fairness auditing, accountability chains in human-agent teams, and regulatory compliance under emerging AI legislation. Deployment sustainability is addressed through discussions of continual causal model updating, computational resource management, and robustness across distributional shifts. The analysis synthesizes perspectives from machine learning, robotics, human-computer interaction, and science and technology studies to provide a multi-dimensional framework for building trustworthy embodied AI infrastructures.
References
1. Pearl, J. (2009). Causality. Cambridge University Press.
2. Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE, 109(5), 612–634.
3. Ha, D., & Schmidhuber, J. (2018). World models. arXiv preprint arXiv:1803.10122.
4. Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2020). Explainable reinforcement learning through a causal lens. Proceedings of the AAAI Conference on Artificial Intelligence, 34(3), 2493–2500.
5. Xiong, Z., Song, Y., Kang, H., Yan, Q., Jiang, L., Yang, J., ... & Jacobs, N. (2026). ActWorld: From Explorable to Interactive World Model via Action-Aware Memory. arXiv preprint arXiv:2606.17730.
6. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43.
7. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
8. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30.
9. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.
10. Hafner, D., Lillicrap, T., Norouzi, M., & Ba, J. (2021). Mastering atari with discrete world models. International Conference on Learning Representations.
11. Buesing, L., Weber, T., Zwols, Y., Racaniere, S., Guez, A., Lespiau, J. B., & Heess, N. (2019). Woulda, coulda, shoulda: Counterfactually-guided policy search. International Conference on Learning Representations.
12. Zhang, J., & Bareinboim, E. (2020). Designing optimal dynamic treatment regimes: A causal reinforcement learning approach. Proceedings of the International Conference on Machine Learning.
13. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
14. Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant risk minimization. arXiv preprint arXiv:1907.02893.
15. Spirtes, P., Glymour, C. N., & Scheines, R. (2000). Causation, prediction, and search. MIT Press.
16. Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., & Pathak, D. (2020). Planning to explore via self-supervised world models. Proceedings of the International Conference on Machine Learning.
17. Duan, J., Yu, S., Tan, H., Zhu, H., & Tan, C. (2022). A survey of embodied AI: From simulators to research tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2), 230–244.
18. Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12(1), 5721.
19. Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations.
20. Zhang, K., Schölkopf, B., Spirtes, P., & Glymour, C. (2018). Learning causality and causality-related learning: Some recent progress. National Science Review, 5(1), 26–29.
21. Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning.
22. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
23. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
24. Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017). Target-driven visual navigation in indoor scenes using deep reinforcement learning. Proceedings of the IEEE International Conference on Robotics and Automation.
25. Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., & Bachem, O. (2019). Challenging common assumptions in the unsupervised learning of disentangled representations. Proceedings of the International Conference on Machine Learning.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
