| 2024 | ICLR | Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. | Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi |
| 2023 | ICLR | Identifiability Results for Multimodal Contrastive Learning. | Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt |
| 2023 | ICML | On the Identifiability and Estimation of Causal Location-Scale Noise Models. | Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schlkopf, Peter Bhlmann, Alexander Marx |
| 2022 | ICML | Inferring Cause and Effect in the Presence of Heteroscedastic Noise. | Sascha Xu, Osman Mian, Alexander Marx, Jilles Vreeken |
| 2022 | SDM | Estimating Mutual Information via Geodesic | Alexander Marx, Jonas Fischer |
| 2021 | AAAI | Discovering Fully Oriented Causal Networks. | Osman Mian, Alexander Marx, Jilles Vreeken |
| 2021 | UAI | A weaker faithfulness assumption based on triple interactions. | Alexander Marx, Arthur Gretton, Joris M. Mooij |
| 2021 | SDM | Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. | Alexander Marx, Lincen Yang, Matthijs van Leeuwen |
| 2019 | AISTATS | Testing Conditional Independence on Discrete Data using Stochastic Complexity. | Alexander Marx, Jilles Vreeken |
| 2019 | KDD | Identifiability of Cause and Effect using Regularized Regression. | Alexander Marx, Jilles Vreeken |
| 2017 | ICDM | Telling Cause from Effect Using MDL-Based Local and Global Regression. | Alexander Marx, Jilles Vreeken |