Causal inference at the Max-Planck-Institute for Intelligent Systems Tübingen.
The research of our group aims at developing novel methods for inferring causal relations from data. For a detailed description
see
Members of the causality group:
Some publications and the available source code for them:
- Janzing and Schölkopf (2018): Detecting non-causal artifacts in multivariate linear regression models , code
- Janzing and Schölkopf (2017): Detecting confounding in multivariate linear models via spectral analysis , code
- Steudel, Janzing and Schölkopf (2010): Causal Markov condition for submodular information measures, code
- Zhang, Schölkopf and Janzing (2010): Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
- Peters, Janzing and Schölkopf (2010): Identifying Cause and Effect on Discrete Data using Additive Noise Models, code
- Peters, Janzing, Gretton and Schölkopf (2009): Detecting the Direction of Causal Time Series , code
- Janzing, Hoyer and Schölkopf (2010): Telling cause from effect based on high-dimensional observations, code
- Zhang and Hyvärinen (2009): Distinguishing cause from effect with constrained nonlinear ICA, code
- Hoyer, Janzing, Mooij, Peters, Schölkopf (2008): Nonlinear causal discovery with additive noise models, code
- Daniušis, Janzing, Mooij, Zscheischler, Steudel, Zhang, Schölkopf (2010): Inferring deterministic causal relations, code
- Mooij, Stegle, Janzing, Zhang, Schölkopf (2010): Probabilistic latent variable models for distinguishing between cause and effect, code
- Peters, Mooij, Janzing, Schölkopf (2011): Identifiability of Causal Graphs using Functional Models, code
- Zscheischler, Janzing, Zhang (2011): Testing whether linear relations are causal: A free probability approach, code
- Zhang, Peters, Janzing and Schölkopf (2011): Kernel-based conditional independence test and application in causal discovery, code
- Mooij, Janzing, Heskes and Schölkopf (2011): On Causal Discovery with Cyclic Additive Noise Models, code
- Janzing, Balduzzi, Grosse-Wentrup, Schölkopf (2013):
Quantifying causal influences ,
Supplement ,
code
- Janzing, Sgouritsa, Stegle, Peters and Schölkopf (2011): Detecting low-complexity unobserved causes, code
- Sgouritsa, Janzing, Peters and Schölkopf (2013): Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Cause-effect pairs dataset:
We are building a benchmark data set for causal discovery algorithms which focuses on the
two-variable case. If you have interesting data that you would like to contribute please contact Dominik Janzing.