Research

Causality is the science of cause-and-effect. Causal models don't just measure that two events are correlated, or that they appear at the same time, but rather that the occurrence of one event leads to the next.

My main research focuses on using ideas from causality to develop better AI systems. Building causality into the equation is important because it allows us to identify what the root causes of the problem are, so that we can focus on curing diseases instead of just the symptoms.

Research interests:

Bayesian inference
Causality
Explainable machine learning
AI for Healthcare
Statistical signal processing

Keywords for nerds: Gaussian processes, causal models, manifolds, time series, DAGs, differentiation, state spaces, anomaly detection, feature attributions, counterfactual queries, confounders, interaction effects, neuroscience

A plot showing a shadow manifold embedded in 3 dimensional space.

Publications

Here is a running list of my peer-reviewed publications and links to their associated materials.

Journal

AI Foundations: Building Essential Machine Learning Skills and Understanding
Kurt Butler, Monica Bugallo, and Petar M. Djuric
Accepted for publication in the IEEE Signal Processing Magazine, 2025
Computer vision detects covert voluntary facial movements in unresponsive brain injury patients
Xi Cheng, Sujith Swarna, Jermaine Robertson, Nathaniel A Cleri, Jordan R Saadon, Chiemeka Uwakwe, Yindong Hua, Seyed Morsal Mosallami Aghili, Cassie Wang, Robert S Kleyner, Xuwen Zheng, Ariana Forohar, John Servider, Kurt Butler, Chao Chen, Jordane Dimidschstein, Petar M Djurić, Charles B Mikell, Sima Mofakham
Communications Medicine, 2025
[paper]
Fast and Slow Recovery of Consciousness Following Traumatic Brain Injury
Sujith Swarna, Jordan R Saadon, Jermaine Robertson, Vaibhav Vagal, Nathaniel A Cleri, Kurt Butler, Xi Cheng, Yindong Hua, Seyed Morsal Mosallami Aghili, Chiemeka Uwakwe, Jason Zhang, Xuwen Zheng, Aniket Singh, Cassie Wang, Thomas Hagan, Chuan Huang, Petar M Djurić, Charles B Mikell, Sima Mofakham
Neurocritical Care, 2025
[paper]
Counterfactual Reasoning with Vector Autoregressive Models
Kurt Butler, Marija Iloska, and Petar M. Djuric
Science Talks, 2025
[video article]
Measuring Strength of Joint Causal Effects
Kurt Butler, Guanchao Feng, and Petar M. Djuric
IEEE Transactions on Signal Processing, 2024
[paper][Github]
Learning the hierarchical organization of the frontal lobe with differential causal effects
Kurt Butler, Duncan Cleveland, Charles B. Mikell, Sima Mofakham, Yuri B. Saalmann, and Petar M. Djuric
Science Talks, 2024
[video article]
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, and Petar M. Djuric
IEEE Open Journal of Signal Processing, 2024
[paper] [arXiv] [Github]
On Causal Discovery with Convergent Cross Mapping
Kurt Butler, Guanchao Feng, and Petar M. Djuric
IEEE Transactions on Signal Processing, 2023
[paper] [Github]
A Differential Measure of the Strength of Causation
Kurt Butler, Guanchao Feng, and Petar M. Djuric
IEEE Signal Processing Letters, 2022
[paper] [Github]

Conference

Trustworthy Prediction with Gaussian Process Knowledge Scores
Kurt Butler, Guanchao Feng, Tong Chen, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Palermo, Italy, 2025
[arXiv][Github]
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Kurt Butler, Daniel Waxman, and Petar M. Djuric
Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
[paper][arXiv][Github]
On Counterfactual Interventions in Vector Autoregressive Models
Kurt Butler, Marija Iloska, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Lyon, France, 2024
[paper] [Github]
Gaussian process-based inference of brain functional connectivity
Chen Cui, Sima Mofakham, Jessica M. Phillips, Kurt Butler, Charles B. Mikell, Yuri B. Saalmann, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Lyon, France, 2024
[paper]
Sequential Detection of Anomalies in Noisy Outputs of an Unknown Function using Gaussian and Yule-Simon Processes
Liu Yang, Kurt Butler, and Petar M. Djuric
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, South Korea, 2024
[paper]
An Approach to Learning the Hierarchical Organization of the Frontal Lobe
Kurt Butler, Duncan Cleveland, Charles B. Mikell, Sima Mofakham, Yuri B. Saalmann, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Helsinki, Finland, 2023
[paper]
Detecting Confounders in Multivariate Time Series using Strength of Causation
Yuhao Liu, Chen Cui, Daniel Waxman, Kurt Butler, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Helsinki, Finland, 2023
[paper]
Predicting latent states of dynamical systems with state-space reconstruction and Gaussian processes
Kurt Butler, Guanchao Feng, Charles B. Mikell, Sima Mofakham, and Petar M. Djuric
Proceedings of the European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, 2022
[paper]

Preprint

A Shift in Perspective on Causality in Domain Generalization
Damian Machlanski, Stephanie Riley, Edward Moroshko, Kurt Butler, Panagiotis Dimitrakopoulos, Thomas Melistas, Akchunya Chanchal, Steven McDonagh, Ricardo Silva, Sotirios A. Tsaftaris
arXiv, 2025
[arXiv]
Explainable Learning with Gaussian Processes
Kurt Butler, Guanchao Feng, and Petar M. Djuric
arXiv, 2024
[arXiv] [Github]

Miscellaneous

Causal Inference From Time Series: Methods for Discovering, Explaining, and Estimating Causal Relationships
Ph.D. Thesis, State University of New York at Stony Brook, 2024
[EURASIP Library]

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