Avoiding Discrimination through Causal Reasoning. - Niki Kilbertus; Mateo Rojas-Carulla; Giambattista Parascandolo; Moritz Hardt; Dominik Janzing; Bernhard Schölkopf (NIPS, 2017)

Blind Justice: Fairness with Encrypted Sensitive Attributes. - Niki Kilbertus; Adrià Gascón; Matt J. Kusner; Michael Veale; Krishna P. Gummadi; Adrian Weller (ICML, 2018)

Learning Independent Causal Mechanisms. - Giambattista Parascandolo; Niki Kilbertus; Mateo Rojas-Carulla; Bernhard Schölkopf (ICML, 2018)

Generalization in anti-causal learning. - Niki Kilbertus; Giambattista Parascandolo; Bernhard Schölkopf (CoRR, 2018)

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding. - Niki Kilbertus; Philip J. Ball; Matt J. Kusner; Adrian Weller; Ricardo Silva 001 (UAI, 2019)

Improving Consequential Decision Making under Imperfect Predictions. - Niki Kilbertus; Manuel Gomez-Rodriguez; Bernhard Schölkopf; Krikamol Muandet; Isabel Valera (CoRR, 2019)

Convolutional neural networks: a magic bullet for gravitational-wave detection? - Timothy D. Gebhard; Niki Kilbertus; Ian Harry; Bernhard Schölkopf (CoRR, 2019)

Fair Decisions Despite Imperfect Predictions. - Niki Kilbertus; Manuel Gomez Rodriguez; Bernhard Schölkopf; Krikamol Muandet; Isabel Valera (AISTATS, 2020)

A Class of Algorithms for General Instrumental Variable Models. - Niki Kilbertus; Matt J. Kusner; Ricardo Silva 001 (NeurIPS, 2020)

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Anirudh Goyal; Francesco Locatello; Bernhard Schölkopf; Stefan Bauer (CoRR, 2020)

Exploration in two-stage recommender systems. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus (CoRR, 2020)

Beyond traditional assumptions in fair machine learning - Niki Kilbertus (2020)

On Disentangled Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Francesco Locatello; Andrea Dittadi; Anirudh Goyal; Bernhard Schölkopf; Stefan Bauer (ICML, 2021)

On Component Interactions in Two-Stage Recommender Systems. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus (NeurIPS, 2021)

Beyond traditional assumptions in fair machine learning. - Niki Kilbertus (CoRR, 2021)

A causal view on compositional data. - Elisabeth Ailer; Christian L. Müller; Niki Kilbertus (CoRR, 2021)

Beyond Predictions in Neural ODEs: Identification and Interventions. - Hananeh Aliee; Fabian J. Theis; Niki Kilbertus (CoRR, 2021)

Multi-disciplinary fairness considerations in machine learning for clinical trials. - Isabel Chien; Nina Deliu; Richard E. Turner; Adrian Weller; Sofia S. Villar; Niki Kilbertus (FAccT, 2022)

Sparsity in Continuous-Depth Neural Networks. - Hananeh Aliee; Till Richter; Mikhail Solonin; Ignacio Ibarra; Fabian J. Theis; Niki Kilbertus (NeurIPS, 2022)

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. - Leon Hetzel; Simon Böhm; Niki Kilbertus; Stephan Günnemann; Mohammad Lotfollahi; Fabian J. Theis (NeurIPS, 2022)

Numerical Analysis of the Causal Action Principle in Low Dimensions. - Felix Finster; Robert H. Jonsson; Niki Kilbertus (CoRR, 2022)

Predicting single-cell perturbation responses for unseen drugs. - Leon Hetzel; Simon Böhm; Niki Kilbertus; Stephan Günnemann; Mohammad Lotfollahi; Fabian J. Theis (CoRR, 2022)

Discovering ordinary differential equations that govern time-series. - Sören Becker; Michal Klein; Alexander Neitz; Giambattista Parascandolo; Niki Kilbertus (CoRR, 2022)

Stochastic Causal Programming for Bounding Treatment Effects. - Kirtan Padh; Jakob Zeitler; David S. Watson; Matt J. Kusner; Ricardo Silva 001; Niki Kilbertus (CLeaR, 2023)

Modeling content creator incentives on algorithm-curated platforms. - Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus; Sarah Dean (ICLR, 2023)

Sequential Underspecified Instrument Selection for Cause-Effect Estimation. - Elisabeth Ailer; Jason S. Hartford; Niki Kilbertus (ICML, 2023)

Predicting Ordinary Differential Equations with Transformers. - Sören Becker; Michal Klein; Alexander Neitz; Giambattista Parascandolo; Niki Kilbertus (ICML, 2023)

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints. - Alistair White; Niki Kilbertus; Maximilian Gelbrecht; Niklas Boers (NeurIPS, 2023)

Stabilized Neural Differential Equations for Learning Constrained Dynamics. - Alistair White; Niki Kilbertus; Maximilian Gelbrecht; Niklas Boers (CoRR, 2023)

Supervised learning and model analysis with compositional data. - Shimeng Huang; Elisabeth Ailer; Niki Kilbertus; Niklas Pfister (PLoS Comput. Biol., 2023)

Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation. - Luca Eyring; Dominik Klein 005; Théo Uscidda; Giovanni Palla; Niki Kilbertus; Zeynep Akata; Fabian J. Theis (ICLR, 2024)

ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. - Stéphane d'Ascoli; Sören Becker; Philippe Schwaller; Alexander Mathis; Niki Kilbertus (ICLR, 2024)

Towards Physically Consistent Deep Learning For Climate Model Parameterizations. - Birgit Kühbacher; Fernando Iglesias-Suarez; Niki Kilbertus; Veronika Eyring (ICMLA, 2024)

Targeted Sequential Indirect Experiment Design. - Elisabeth Ailer; Niclas Dern; Jason S. Hartford; Niki Kilbertus (NeurIPS, 2024)

Causal machine learning for predicting treatment outcomes. - Stefan Feuerriegel; Dennis Frauen; Valentyn Melnychuk; Jonas Schweisthal; Konstantin Hess; Alicia Curth; Stefan Bauer; Niki Kilbertus; Isaac S. Kohane; Mihaela van der Schaar (CoRR, 2024)

Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series. - Thomas Schwarz; Cecilia Casolo; Niki Kilbertus (CoRR, 2024)

Learning Representations of Instruments for Partial Identification of Treatment Effects. - Jonas Schweisthal; Dennis Frauen; Maresa Schröder; Konstantin Hess; Niki Kilbertus; Stefan Feuerriegel (CoRR, 2024)

Projected Neural Differential Equations for Learning Constrained Dynamics. - Alistair White; Anna Büttner; Maximilian Gelbrecht; Valentin Duruisseaux; Niki Kilbertus; Frank Hellmann; Niklas Boers (CoRR, 2024)

Generative Intervention Models for Causal Perturbation Modeling. - Nora Schneider; Lars Lorch; Niki Kilbertus; Bernhard Schölkopf; Andreas Krause 001 (CoRR, 2024)

Learning Counterfactually Invariant Predictors. - Francesco Quinzan; Cecilia Casolo; Krikamol Muandet; Yucen Luo; Niki Kilbertus (Trans. Mach. Learn. Res., 2024)

Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity. - Zhufeng Li; Sandeep Suresh Cranganore; Nicholas D. Youngblut; Niki Kilbertus (AAAI, 2025)

An Asymmetric Independence Model for Causal Discovery on Path Spaces. - Georg Manten; Cecilia Casolo; Søren Wengel Mogensen; Niki Kilbertus (CLeaR, 2025)

Your Assumed DAG is Wrong And Here's How To Deal With It. - Kirtan Padh; Zhufeng Li; Cecilia Casolo; Niki Kilbertus (CLeaR, 2025)

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes. - Georg Manten; Cecilia Casolo; Emilio Ferrucci; Søren Wengel Mogensen; Cristopher Salvi; Niki Kilbertus (ICLR, 2025)

Identifiability Challenges in Sparse Linear Ordinary Differential Equations. - Cecilia Casolo; Sören Becker; Niki Kilbertus (CoRR, 2025)