Dominik Janzing

Falsifying Causal Graphs With Outlier Events UAI 2026 IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery CLeaR 2026 Cross-validating causal discovery via Leave-One-Variable-Out CLeaR 2025 Toward Falsifying Causal Graphs Using a Permutation-Based Test AAAI 2025 Toward Universal Laws of Outlier Propagation UAI 2025 Causal vs. Anticausal merging of predictors NeurIPS 2024 DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models JMLR 2024 Meaningful Causal Aggregation and Paradoxical Confounding CLeaR 2024 Quantifying intrinsic causal contributions via structure preserving interventions AISTATS 2024 Self-Compatibility: Evaluating Causal Discovery without Ground Truth AISTATS 2024 Assumption violations in causal discovery and the robustness of score matching NeurIPS 2023 Causal information splitting: Engineering proxy features for robustness to distribution shifts UAI 2023 Causal forecasting: generalization bounds for autoregressive models UAI 2022 Causal Inference Through the Structural Causal Marginal Problem ICML 2022 Causal structure-based root cause analysis of outliers ICML 2022 Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations CLeaR 2022 Obtaining Causal Information by Merging Datasets with MAXENT AISTATS 2022 On Measuring Causal Contributions via do-interventions ICML 2022 Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models ICML 2022 Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies AISTATS 2022 You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction ICLR 2022 A Theory of Independent Mechanisms for Extrapolation in Generative Models AAAI 2021 Necessary and sufficient conditions for causal feature selection in time series with latent common causes ICML 2021 Why did the distribution change? AISTATS 2021 Feature relevance quantification in explainable AI: A causal problem AISTATS 2020 Causal Regularization NeurIPS 2019 Perceiving the arrow of time in autoregressive motion NeurIPS 2019 Selecting causal brain features with a single conditional independence test per feature NeurIPS 2019 Cause-Effect Inference by Comparing Regression Errors AISTATS 2018 Detecting non-causal artifacts in multivariate linear regression models ICML 2018 Group Invariance Principles for Causal Generative Models AISTATS 2018 Avoiding Discrimination through Causal Reasoning NeurIPS 2017 Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks JMLR 2016 Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components ICML 2015 Inference of Cause and Effect with Unsupervised Inverse Regression AISTATS 2015 Removing systematic errors for exoplanet search via latent causes ICML 2015 Semi-Supervised Interpolation in an Anticausal Learning Scenario JMLR 2015 Telling cause from effect in deterministic linear dynamical systems ICML 2015 Causal Discovery with Continuous Additive Noise Models JMLR 2014 Consistency of Causal Inference under the Additive Noise Model ICML 2014 Causal Inference on Time Series using Restricted Structural Equation Models NeurIPS 2013 On Causal Discovery with Cyclic Additive Noise Models NeurIPS 2011 Identifying Cause and Effect on Discrete Data using Additive Noise Models AISTATS 2010 Probabilistic latent variable models for distinguishing between cause and effect NeurIPS 2010 Nonlinear causal discovery with additive noise models NeurIPS 2008