Dominik Janzing
57 publications
11 venues
H Index 23
Affiliation
Max Planck Institute for Intelligent Systems, T bingen , GermanyLinks
| Name | Venue | Year | citations |
|---|---|---|---|
Causal information splitting: Engineering proxy features for robustness to distribution shifts.
|
UAI | 2023 | 0 |
| Assumption violations in causal discovery and the robustness of score matching.
|
NIPS/NeurIPS | 2023 | 0 |
| Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies.
|
AISTATS | 2022 | 1 |
| On Measuring Causal Contributions via do-interventions.
|
ICML | 2022 | 3 |
| Causal Inference Through the Structural Causal Marginal Problem.
|
ICML | 2022 | 5 |
| Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models.
|
ICML | 2022 | 10 |
| Causal structure-based root cause analysis of outliers.
|
ICML | 2022 | 0 |
| Causal forecasting: generalization bounds for autoregressive models.
|
UAI | 2022 | 0 |
| Obtaining Causal Information by Merging Datasets with MAXENT.
|
AISTATS | 2022 | 0 |
| You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.
|
ICLR | 2022 | 0 |
| Why did the distribution change?
|
AISTATS | 2021 | 10 |
| A Theory of Independent Mechanisms for Extrapolation in Generative Models.
|
AAAI | 2021 | 0 |
| Necessary and sufficient conditions for causal feature selection in time series with latent common causes.
|
ICML | 2021 | 0 |
| Feature relevance quantification in explainable AI: A causal problem.
|
AISTATS | 2020 | 0 |
| Perceiving the arrow of time in autoregressive motion.
|
NIPS/NeurIPS | 2019 | 2 |
| Causal Regularization.
|
NIPS/NeurIPS | 2019 | 4 |
| Selecting causal brain features with a single conditional independence test per feature.
|
NIPS/NeurIPS | 2019 | 8 |
| Detecting non-causal artifacts in multivariate linear regression models.
|
ICML | 2018 | 22 |
| Cause-Effect Inference by Comparing Regression Errors.
|
AISTATS | 2018 | 55 |
| Group invariance principles for causal generative models.
|
AISTATS | 2018 | 0 |
| Causal Consistency of Structural Equation Models.
|
UAI | 2017 | 70 |
| Avoiding Discrimination through Causal Reasoning.
|
NIPS/NeurIPS | 2017 | 457 |
| Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.
|
JMLR | 2016 | 0 |
| Semi-supervised interpolation in an anticausal learning scenario.
|
JMLR | 2015 | 21 |
| Removing systematic errors for exoplanet search via latent causes.
|
ICML | 2015 | 10 |
| Inference of Cause and Effect with Unsupervised Inverse Regression.
|
AISTATS | 2015 | 65 |
| Telling cause from effect in deterministic linear dynamical systems.
|
ICML | 2015 | 45 |
| Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components.
|
ICML | 2015 | 0 |
| Causal discovery with continuous additive noise models.
|
JMLR | 2014 | 0 |
| Inferring latent structures via information inequalities.
|
UAI | 2014 | 44 |
| Estimating Causal Effects by Bounding Confounding.
|
UAI | 2014 | 10 |
| Consistency of Causal Inference under the Additive Noise Model.
|
ICML | 2014 | 0 |
| Causal Inference on Time Series using Restricted Structural Equation Models.
|
NIPS/NeurIPS | 2013 | 102 |
| From Ordinary Differential Equations to Structural Causal Models: the deterministic case.
|
UAI | 2013 | 85 |
| Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders.
|
UAI | 2013 | 16 |
| On causal and anticausal learning.
|
ICML | 2012 | 424 |
| Information-geometric approach to inferring causal directions.
|
Artificial Intelligence | 2012 | 250 |
| On Causal Discovery with Cyclic Additive Noise Models.
|
NIPS/NeurIPS | 2011 | 85 |
| Identifiability of Causal Graphs using Functional Models.
|
UAI | 2011 | 125 |
| Detecting low-complexity unobserved causes.
|
UAI | 2011 | 22 |
| Testing whether linear equations are causal: A free probability theory approach.
|
UAI | 2011 | 36 |
| Kernel-based Conditional Independence Test and Application in Causal Discovery.
|
UAI | 2011 | 441 |
| Causal Inference on Discrete Data Using Additive Noise Models.
|
TPAMI | 2011 | 0 |
| Identifying Cause and Effect on Discrete Data using Additive Noise Models.
|
AISTATS | 2010 | 72 |
| Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery.
|
UAI | 2010 | 13 |
| Inferring deterministic causal relations.
|
UAI | 2010 | 156 |
| Causal Markov Condition for Submodular Information Measures.
|
COLT | 2010 | 25 |
| Probabilistic latent variable models for distinguishing between cause and effect.
|
NIPS/NeurIPS | 2010 | 119 |
| Telling cause from effect based on high-dimensional observations.
|
ICML | 2010 | 0 |
| Identifying confounders using additive noise models.
|
UAI | 2009 | 63 |
| Detecting the direction of causal time series.
|
ICML | 2009 | 43 |
| Regression by dependence minimization and its application to causal inference in additive noise models.
|
ICML | 2009 | 133 |
| Nonlinear causal discovery with additive noise models.
|
NIPS/NeurIPS | 2008 | 744 |
| A kernel-based causal learning algorithm.
|
ICML | 2007 | 64 |
| Exploring the causal order of binary variables via exponential hierarchies of Markov kernels.
|
ESANN | 2007 | 6 |
| Learning causality by identifying common effects with kernel-based dependence measures.
|
ESANN | 2007 | 0 |
| Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions.
|
ESANN | 2007 | 7 |