Journal Articles
Parameter identification in linear non-Gaussian causal models under general confounding, Annals of Statistics 2025 (with M.Drton and J.Etesami), PDF.
Probability Metrics for Tropical Spaces of Different Dimensions, Foundations of Data Science 2025 (with R. Talbut, A. Monod, M. Drton, Y. Cao), PDF.
Learning linear Gaussian Polytree models with Interventions, IEEE Journal on Selected Areas in Information Theory, Special Issue: Causality: Fundamental Limits and Applications, 2023 (with E.Duarte, L. Waldmann, M. Drton), PDF.
Conference Papers
Causal Effect Identification in LiNGAM Models from higher order cumulants, ICML 2025 (with M. Drton, N. Kiyavash, Y. Kyvva, S. Salehkaleybar), PDF.
Causal Effect Identification in LiNGAM Models with Latent Confounders, ICML 2024 (with M. Drton, N. Kiyavash, Y. Kyvva, S. Salehkaleybar), PDF.
Learning linear non-Gaussian Polytree models, UAI 2022 (with M. Drton and A. Monod), PDF.
Selected Talks
Learning linear non-Gaussian Polytree models, ETH-UCPH-TUM Workshop on Graphical Models.
Learning Gaussian Polytrees with interventions, ICSDS2022, GPSD2023.
Wasserstein Distances on Tropical Projective Torii of different dimensions, SIAM AG 2023.
Parameter identification in linear non-Gaussian causal models under general confounding, European Workshop of Algebraic Statistics and Graphical Models.