Jonas Peters

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Københavns Uni ∙ Max Planck Society ∙ ETH Zurich

Associate Professor in Statistics at University of Copenhagen

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions

Structural causal models (SCMs), also known as non-parametric structural...
11/18/2016 ∙ by Stephan Bongers, et al. ∙ 0 ∙ share

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Kernel-based Tests for Joint Independence

We investigate the problem of testing whether d random variables, which ...
03/01/2016 ∙ by Niklas Pfister, et al. ∙ 0 ∙ share

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backShift: Learning causal cyclic graphs from unknown shift interventions

We propose a simple method to learn linear causal cyclic models in the p...
06/08/2015 ∙ by Dominik Rothenhäusler, et al. ∙ 0 ∙ share

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Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to ...
05/12/2015 ∙ by Bernhard Schölkopf, et al. ∙ 0 ∙ share

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Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations

We describe a method to perform functional operations on probability dis...
01/27/2015 ∙ by Bernhard Schölkopf, et al. ∙ 0 ∙ share

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Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...
12/11/2014 ∙ by Joris M. Mooij, et al. ∙ 0 ∙ share

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Counterfactual Reasoning and Learning Systems

This work shows how to leverage causal inference to understand the behav...
09/11/2012 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

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On the Intersection Property of Conditional Independence and its Application to Causal Discovery

This work investigates the intersection property of conditional independ...
03/03/2014 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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CAM: Causal additive models, high-dimensional order search and penalized regression

We develop estimation for potentially high-dimensional additive structur...
10/06/2013 ∙ by Peter Bühlmann, et al. ∙ 0 ∙ share

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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

We propose a kernel method to identify finite mixtures of nonparametric ...
09/26/2013 ∙ by Eleni Sgouritsa, et al. ∙ 0 ∙ share

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Causal Discovery with Continuous Additive Noise Models

We consider the problem of learning causal directed acyclic graphs from ...
09/26/2013 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Structural Intervention Distance (SID) for Evaluating Causal Graphs

Causal inference relies on the structure of a graph, often a directed ac...
06/05/2013 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Causal Inference on Time Series using Structural Equation Models

Causal inference uses observations to infer the causal structure of the ...
07/21/2012 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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On Causal and Anticausal Learning

We consider the problem of function estimation in the case where an unde...
06/27/2012 ∙ by Bernhard Schoelkopf, et al. ∙ 0 ∙ share

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Identifiability of Gaussian structural equation models with equal error variances

We consider structural equation models in which variables can be written...
05/11/2012 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Identifying confounders using additive noise models

We propose a method for inferring the existence of a latent common cause...
05/09/2012 ∙ by Dominik Janzing, et al. ∙ 0 ∙ share

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Detecting low-complexity unobserved causes

We describe a method that infers whether statistical dependences between...
02/14/2012 ∙ by Dominik Janzing, et al. ∙ 0 ∙ share

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Identifiability of Causal Graphs using Functional Models

This work addresses the following question: Under what assumptions on th...
02/14/2012 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Kernel-based Conditional Independence Test and Application in Causal Discovery

Conditional independence testing is an important problem, especially in ...
02/14/2012 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Robust Learning via Cause-Effect Models

We consider the problem of function estimation in the case where the dat...
12/12/2011 ∙ by Bernhard Schölkopf, et al. ∙ 0 ∙ share

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Causal Inference on Discrete Data using Additive Noise Models

Inferring the causal structure of a set of random variables from a finit...
11/02/2009 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Anchor regression: heterogeneous data meets causality

This is a preliminary draft of "Anchor regression: heterogeneous data me...
01/18/2018 ∙ by Dominik Rothenhäusler, et al. ∙ 0 ∙ share

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The Hardness of Conditional Independence Testing and the Generalised Covariance Measure

It is a common saying that testing for conditional independence, i.e., t...
04/19/2018 ∙ by Rajen D. Shah, et al. ∙ 0 ∙ share

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Switching Regression Models and Causal Inference in the Presence of Latent Variables

Given a response Y and a vector X = (X^1, ..., X^d) of d predictors, we ...
08/16/2018 ∙ by Rune Christiansen, et al. ∙ 0 ∙ share

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Identifying Causal Structure in Large-Scale Kinetic Systems

In the natural sciences, differential equations are widely used to descr...
10/28/2018 ∙ by Niklas Pfister, et al. ∙ 0 ∙ share

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Causal discovery in heavy-tailed models

Causal questions are omnipresent in many scientific problems. While much...
08/14/2019 ∙ by Nicola Gnecco, et al. ∙ 0 ∙ share

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Stabilizing Variable Selection and Regression

We consider regression in which one predicts a response Y with a set of ...
11/05/2019 ∙ by Niklas Pfister, et al. ∙ 0 ∙ share

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Causal models for dynamical systems

A probabilistic model describes a system in its observational state. In ...
01/17/2020 ∙ by Jonas Peters, et al. ∙ 0 ∙ share

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Distributional robustness as a guiding principle for causality in cognitive neuroscience

While probabilistic models describe the dependence structure between obs...
02/14/2020 ∙ by Sebastian Weichwald, et al. ∙ 0 ∙ share

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Jonas Peters

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