Research Group Mathias Drton
Mathias Drton
Prof. Dr.
Principal Investigator
Mathematical Statistics
Mathias Drton
is Professor of Mathematical Statistics at TU Munich.
He works on methods and theory in the field of statistics. His main interest is the analysis of complex multivariate data. The main focus lies on graphical models, which capture fine causal relationships and find manifold applications in modern data-driven science. The aim of the research is, amongst others, to clarify by means of algebraic and probability theory under which circumstances data allow conclusions about causal relationships and to develop efficient methods for their estimation.
Team members @MCML
Publications @MCML
[7]
D. Tramontano, Y. Kivva, S. Salehkaleybar, M. Drton and N. Kiyavash.
Causal Effect Identification in LiNGAM Models with Latent Confounders.
41st International Conference on Machine Learning (ICML 2024). Vienna, Austria, Jul 21-27, 2024. URL.
Causal Effect Identification in LiNGAM Models with Latent Confounders.
41st International Conference on Machine Learning (ICML 2024). Vienna, Austria, Jul 21-27, 2024. URL.
MCML Authors
[6]
N. Sturma, C. Squires, M. Drton and C. Uhler.
Unpaired Multi-Domain Causal Representation Learning.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec 10-16, 2023. URL.
Unpaired Multi-Domain Causal Representation Learning.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, Dec 10-16, 2023. URL.
MCML Authors
[5]
D. Strieder and M. Drton.
Confidence in causal inference under structure uncertainty in linear causal models with equal variances.
Journal of Causal Inference 11.1 (Dec. 2023). DOI.
Confidence in causal inference under structure uncertainty in linear causal models with equal variances.
Journal of Causal Inference 11.1 (Dec. 2023). DOI.
MCML Authors
[4]
G. Keropyan, D. Strieder and M. Drton.
Rank-Based Causal Discovery for Post-Nonlinear Models.
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023). Valencia, Spain, Apr 25-27, 2023. URL.
Rank-Based Causal Discovery for Post-Nonlinear Models.
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023). Valencia, Spain, Apr 25-27, 2023. URL.
MCML Authors
[3]
M. Drton, H. Shi and D. Strieder.
Discussion of “A note on universal inference” by Timmy Tse and Anthony Davison.
Stat 12.1 (Apr. 2023). DOI.
Discussion of “A note on universal inference” by Timmy Tse and Anthony Davison.
Stat 12.1 (Apr. 2023). DOI.
MCML Authors
[2]
R. Foygel Barber, M. Drton, N. Sturma and L. Weihs.
Half-trek criterion for identifiability of latent variable models.
Annals of Statistics 50.6 (Dec. 2022). DOI.
Half-trek criterion for identifiability of latent variable models.
Annals of Statistics 50.6 (Dec. 2022). DOI.
MCML Authors
[1]
D. Strieder and M. Drton.
On the choice of the splitting ratio for the split likelihood ratio test.
Electronic Journal of Statistics 16.2 (Mar. 2022). DOI.
On the choice of the splitting ratio for the split likelihood ratio test.
Electronic Journal of Statistics 16.2 (Mar. 2022). DOI.
MCML Authors
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