Batch-wise Regularization of Deep Neural Networks for Interpretability. - Nadia Burkart; Philipp Michael Faller; Elisabeth Peinsipp; Marco F. Huber (MFI, 2020)

Causal Forecasting: Generalization Bounds for Autoregressive Models. - Leena C. Vankadara; Philipp Michael Faller; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (CoRR, 2021)

Causal forecasting: generalization bounds for autoregressive models. - Leena Chennuru Vankadara; Philipp Michael Faller; Michaela Hardt; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (UAI, 2022)

Reinterpreting causal discovery as the task of predicting unobserved joint statistics. - Dominik Janzing; Philipp Michael Faller; Leena Chennuru Vankadara (CoRR, 2023)

Self-Compatibility: Evaluating Causal Discovery without Ground Truth. - Philipp Michael Faller; Leena C. Vankadara; Atalanti-Anastasia Mastakouri; Francesco Locatello; Dominik Janzing (AISTATS, 2024)

Quantifying intrinsic causal contributions via structure preserving interventions. - Dominik Janzing; Patrick Blöbaum; Atalanti-Anastasia Mastakouri; Philipp Michael Faller; Lenon Minorics; Kailash Budhathoki (AISTATS, 2024)

Score matching through the roof: linear, nonlinear, and latent variables causal discovery. - Francesco Montagna; Philipp Michael Faller; Patrick Blöbaum; Elke Kirschbaum; Francesco Locatello (CLeaR, 2025)

Cross-validating causal discovery via Leave-One-Variable-Out. - Daniela Schkoda; Philipp Michael Faller; Dominik Janzing; Patrick Blöbaum (CLeaR, 2025)

On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models. - Philipp Michael Faller; Dominik Janzing (CoRR, 2025)