Published in Transactions on Machine Learning Research, 2025
Diffusion-based causal discovery for temporal data by integrating multiple causal orderings.
Recommended citation: Sanchez, P., Machlanski, D., McDonagh, S., & Tsaftaris, S. A. (2025). "Causal Ordering for Structure Learning from Time Series." Transactions on Machine Learning Research.
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Published in IEEE Access, 2024
Using Generative Trees to generate new samples and undersmooth downstream causal estimators.
Recommended citation: Machlanski, D., Samothrakis, S., Clarke, P. (2024). "Undersmoothing Causal Estimators With Generative Trees." IEEE Access. vol. 12, pp. 38562-38574.
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Published in 59th Asilomar Conference on Signals, Systems, and Computers (2025), 2025
Diffusion-based causal discovery for temporal data by integrating multiple causal orderings.
Recommended citation: Butler, K., Machlanski, D., Dimitrakopoulos, P., & Tsaftaris, S. A. (2025, November). "Rethinking Chronological Causal Discovery with Signal Processing." In 2025 59th Asilomar Conference on Signals, Systems, and Computers. Institute of Electrical and Electronics Engineers.
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Published in Third Conference on Causal Learning and Reasoning (CLeaR), 2024
Investigating robustness of causal discovery methods to (bad) hyperparameter selection.
Recommended citation: Machlanski, D., Samothrakis, S., Clarke, P. (2024). "Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice." Proceedings of the Third Conference on Causal Learning and Reasoning. PMLR 236:703-739.
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