Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. - Julius von Kügelgen; Alexander Mey; Marco Loog (CoRR, 2018)

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. - Julius von Kügelgen; Alexander Mey; Marco Loog (AISTATS, 2019)

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. - Julius von Kügelgen; Paul K. Rubenstein; Bernhard Schölkopf; Adrian Weller (CoRR, 2019)

Towards Causal Algorithmic Recourse. - Amir-Hossein Karimi; Julius von Kügelgen; Bernhard Schölkopf; Isabel Valera (xxAI@ICML, 2020)

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. - Amir-Hossein Karimi; Bodo Julius von Kügelgen; Bernhard Schölkopf; Isabel Valera (NeurIPS, 2020)

Semi-supervised learning, causality, and the conditional cluster assumption. - Julius von Kügelgen; Alexander Mey; Marco Loog; Bernhard Schölkopf (UAI, 2020)

Towards causal generative scene models via competition of experts. - Julius von Kügelgen; Ivan Ustyuzhaninov; Peter V. Gehler; Matthias Bethge; Bernhard Schölkopf (CoRR, 2020)

A bacterial size law revealed by a coarse-grained model of cell physiology. - François Bertaux; Julius von Kügelgen; Samuel Marguerat; Vahid Shahrezaei (PLoS Comput. Biol., 2020)

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. - Zhijing Jin 001; Julius von Kügelgen; Jingwei Ni; Tejas Vaidhya; Ayush Kaushal; Mrinmaya Sachan; Bernhard Schölkopf (EMNLP (1), 2021)

Independent mechanism analysis, a new concept? - Luigi Gresele; Julius von Kügelgen; Vincent Stimper; Bernhard Schölkopf; Michel Besserve (NeurIPS, 2021)

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. - Julius von Kügelgen; Yash Sharma 001; Luigi Gresele; Wieland Brendel; Bernhard Schölkopf; Michel Besserve; Francesco Locatello (NeurIPS, 2021)

Backward-Compatible Prediction Updates: A Probabilistic Approach. - Frederik Träuble; Julius von Kügelgen; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Peter V. Gehler (NeurIPS, 2021)

Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. - Julius von Kügelgen; Nikita Agarwal; Jakob Zeitler; Afsaneh Mastouri; Bernhard Schölkopf (CoRR, 2021)

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects. - Julius von Kügelgen; Luigi Gresele; Bernhard Schölkopf (IEEE Trans. Artif. Intell., 2021)

On the Fairness of Causal Algorithmic Recourse. - Julius von Kügelgen; Amir-Hossein Karimi; Umang Bhatt; Isabel Valera; Adrian Weller; Bernhard Schölkopf (AAAI, 2022)

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. - Osama Makansi; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Dominik Janzing; Thomas Brox; Bernhard Schölkopf (ICLR, 2022)

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. - Lukas Schott; Julius von Kügelgen; Frederik Träuble; Peter Vincent Gehler; Chris Russell 001; Matthias Bethge; Bernhard Schölkopf; Francesco Locatello; Wieland Brendel (ICLR, 2022)

Causal Inference Through the Structural Causal Marginal Problem. - Luigi Gresele; Julius von Kügelgen; Jonas M. Kübler; Elke Kirschbaum; Bernhard Schölkopf; Dominik Janzing (ICML, 2022)

Probable Domain Generalization via Quantile Risk Minimization. - Cian Eastwood; Alexander Robey; Shashank Singh 011; Julius von Kügelgen; Hamed Hassani; George J. Pappas; Bernhard Schölkopf (NeurIPS, 2022)

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. - Ronan Perry; Julius von Kügelgen; Bernhard Schölkopf (NeurIPS, 2022)

Embrace the Gap: VAEs Perform Independent Mechanism Analysis. - Patrik Reizinger; Luigi Gresele; Jack Brady; Julius von Kügelgen; Dominik Zietlow; Bernhard Schölkopf; Georg Martius; Wieland Brendel; Michel Besserve (NeurIPS, 2022)

Active Bayesian Causal Inference. - Christian Toth; Lars Lorch; Christian Knoll 002; Andreas Krause 001; Franz Pernkopf; Robert Peharz; Julius von Kügelgen (NeurIPS, 2022)

On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". - Shubhangi Ghosh; Luigi Gresele; Julius von Kügelgen; Michel Besserve; Bernhard Schölkopf (CoRR, 2022)

From Statistical to Causal Learning. - Bernhard Schölkopf; Julius von Kügelgen (CoRR, 2022)

Evaluating vaccine allocation strategies using simulation-assisted causal modelling. - Armin Kekic; Jonas Dehning; Luigi Gresele; Julius von Kügelgen; Viola Priesemann; Bernhard Schölkopf (CoRR, 2022)

Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal. - Julius von Kügelgen; Luigi Gresele; Bernhard Schölkopf (IEEE DataPort, 2022)

ima-vae. - Patrik Reizinger; Luigi Gresele; Jack Brady; Dominik Zietlow; Julius von Kügelgen; Michel Besserve; Georg Martius; Wieland Brendel; Bernhard Schölkopf (Zenodo, 2022)

Backtracking Counterfactuals. - Julius von Kügelgen; Abdirisak Mohamed; Sander Beckers (CLeaR, 2023)

Unsupervised Object Learning via Common Fate. - Matthias Tangemann; Steffen Schneider 001; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Thomas Brox; Matthias Kümmerer; Matthias Bethge; Bernhard Schölkopf (CLeaR, 2023)

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. - Cian Eastwood; Andrei Liviu Nicolicioiu; Julius von Kügelgen; Armin Kekic; Frederik Träuble; Andrea Dittadi; Bernhard Schölkopf (ICLR, 2023)

Provably Learning Object-Centric Representations. - Jack Brady; Roland S. Zimmermann; Yash Sharma 001; Bernhard Schölkopf; Julius von Kügelgen; Wieland Brendel (ICML, 2023)

Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. - Cian Eastwood; Shashank Singh 011; Andrei Liviu Nicolicioiu; Marin Vlastelica Pogancic; Julius von Kügelgen; Bernhard Schölkopf (NeurIPS, 2023)

Nonparametric Identifiability of Causal Representations from Unknown Interventions. - Julius von Kügelgen; Michel Besserve; Wendong Liang; Luigi Gresele; Armin Kekic; Elias Bareinboim; David M. Blei; Bernhard Schölkopf (NeurIPS, 2023)

Causal Component Analysis. - Wendong Liang; Armin Kekic; Julius von Kügelgen; Simon Buchholz; Michel Besserve; Luigi Gresele; Bernhard Schölkopf (NeurIPS, 2023)

Causal effect estimation from observational and interventional data through matrix weighted linear estimators. - Klaus-Rudolf Kladny; Julius von Kügelgen; Bernhard Schölkopf; Michael Muehlebach (UAI, 2023)

Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. - Cian Eastwood; Julius von Kügelgen; Linus Ericsson; Diane Bouchacourt; Pascal Vincent; Bernhard Schölkopf; Mark Ibrahim (CoRR, 2023)

Independent Mechanism Analysis and the Manifold Hypothesis. - Shubhangi Ghosh; Luigi Gresele; Julius von Kügelgen; Michel Besserve; Bernhard Schölkopf (CoRR, 2023)

Evaluating vaccine allocation strategies using simulation-assisted causal modeling. - Armin Kekic; Jonas Dehning; Luigi Gresele; Julius von Kügelgen; Viola Priesemann; Bernhard Schölkopf (Patterns, 2023)

Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. - Felix Laumann; Julius von Kügelgen; Junhyung Park; Bernhard Schölkopf; Mauricio Barahona (Entropy, 2023)

Multi-View Causal Representation Learning with Partial Observability. - Dingling Yao; Danru Xu; Sébastien Lachapelle; Sara Magliacane; Perouz Taslakian; Georg Martius; Julius von Kügelgen; Francesco Locatello (ICLR, 2024)

A Sparsity Principle for Partially Observable Causal Representation Learning. - Danru Xu; Dingling Yao; Sébastien Lachapelle; Perouz Taslakian; Julius von Kügelgen; Francesco Locatello; Sara Magliacane (ICML, 2024)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment. - Julius von Kügelgen (CoRR, 2024)

Deep Backtracking Counterfactuals for Causally Compliant Explanations. - Klaus-Rudolf Kladny; Julius von Kügelgen; Bernhard Schölkopf; Michael Muehlebach (Trans. Mach. Learn. Res., 2024)

Interaction Asymmetry: A General Principle for Learning Composable Abstractions. - Jack Brady; Julius von Kügelgen; Sébastien Lachapelle; Simon Buchholz; Thomas Kipf; Wieland Brendel (ICLR, 2025)

From Pixels to Components: Eigenvector Masking for Visual Representation Learning. - Alice Bizeul; Thomas M. Sutter; Alain Ryser; Bernhard Schölkopf; Julius von Kügelgen; Julia E. Vogt (CoRR, 2025)

Representation Learning for Distributional Perturbation Extrapolation. - Julius von Kügelgen; Jakob Ketterer; Xinwei Shen; Nicolai Meinshausen; Jonas Peters (CoRR, 2025)