← Authors

Dominik Janzing

43 papers · 2008–2025 · 8 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (17) πŸ—ΊοΈ Taxonomy Completionist (12) 🌟 Keyword Trendsetter Combo (3) 🀝 Dynamic Duo (23) πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (9) πŸ† Grand Slam ❓ The Questioner πŸ—ƒοΈ Keyword Collector (52) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (13) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ’Ž Century Club (43)

Conferences

AISTATS (10) ICML (10) NIPS (10) JMLR (4) CLEAR (3) UAI (3) AAAI (2) ICLR (1)

Top co-authors

Bernhard SchΓΆlkopf (23) Jonas Peters (6) Patrick Bloebaum (6) Patrick BlΓΆbaum (5) Joris M. Mooij (5) Kailash Budhathoki (5) Lenon Minorics (5) Michel Besserve (4) Francesco Locatello (4) Michaela Hardt (3)

Research topics

Statistics (1)

Keywords

causal inference (31) causal discovery (19) additive noise model (8) causal graph (6) time series (5) cause-effect inference (4) granger causality (4) shapley value (3) observational datum (3) structural equation model (3) causal direction (3) additive noise (3) directed acyclic graph (2) linear dynamical system (2) causal feature selection (2) independent component analysis (2) conditional independence test (2) score matching (2) maximum entropy (2) distribution shift (2)

Papers

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