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Elements of Causal Inference: Foundations and Learning Algorithms
- J. Peters, D. Janzing, B. Schölkopf
- Computer Science
- 29 November 2017
Nonlinear causal discovery with additive noise models
- P. Hoyer, D. Janzing, J. Mooij, J. Peters, B. Schölkopf
- Computer ScienceNIPS
- 8 December 2008
TLDR
Causal inference by using invariant prediction: identification and confidence intervals
- J. Peters, Peter Buhlmann, N. Meinshausen
- Computer Science, Mathematics
- 6 January 2015
TLDR
Counterfactual reasoning and learning systems: the example of computational advertising
- L. Bottou, J. Peters, Edward Snelson
- Computer ScienceJ. Mach. Learn. Res.
- 2013
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such…
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
- J. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schölkopf
- Computer ScienceJ. Mach. Learn. Res.
- 11 December 2014
TLDR
Kernel-based Conditional Independence Test and Application in Causal Discovery
- Kun Zhang, J. Peters, D. Janzing, B. Schölkopf
- Computer ScienceUAI
- 14 July 2011
TLDR
Causal discovery with continuous additive noise models
- J. Peters, J. Mooij, D. Janzing, B. Schölkopf
- MathematicsJ. Mach. Learn. Res.
- 26 September 2013
TLDR
CAM: Causal Additive Models, high-dimensional order search and penalized regression
- P. Bühlmann, J. Peters, J. Ernest
- Computer ScienceArXiv
- 6 October 2013
TLDR
Identifiability of Gaussian structural equation models with equal error variances
- J. Peters, Peter Buhlmann
- Mathematics, Computer Science
- 11 May 2012
TLDR
On causal and anticausal learning
- B. Schölkopf, D. Janzing, J. Peters, Eleni Sgouritsa, Kun Zhang, J. Mooij
- Computer ScienceICML
- 26 June 2012
TLDR
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