b'Jonas Peters'

research

∙ 06/19/2023

Effect-Invariant Mechanisms for Policy Generalization

Policy learning is an important component of many real-world learning sy...

0 Sorawit Saengkyongam, et al. ∙

research

∙ 06/01/2023

Unfair Utilities and First Steps Towards Improving Them

Many fairness criteria constrain the policy or choice of predictors. In ...

0 Frederik Hytting Jørgensen, et al. ∙

research

∙ 03/17/2022

Identifiability of Sparse Causal Effects using Instrumental Variables

Exogenous heterogeneity, for example, in the form of instrumental variab...

0 Niklas Pfister, et al. ∙

research

∙ 03/11/2022

Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past

Instrumental variable (IV) regression relies on instruments to infer cau...

0 Nikolaj Thams, et al. ∙

research

∙ 02/12/2022

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning

Questions in causality, control, and reinforcement learning go beyond th...

0 Sebastian Weichwald, et al. ∙

research

∙ 02/03/2022

Exploiting Independent Instruments: Identification and Distribution Generalization

Instrumental variable models allow us to identify a causal function betw...

0 Sorawit Saengkyongam, et al. ∙

research

∙ 02/02/2022

Invariant Ancestry Search

Recently, methods have been proposed that exploit the invariance of pred...

0 Phillip B Mogensen, et al. ∙

research

∙ 08/19/2021

Structure Learning for Directed Trees

Knowing the causal structure of a system is of fundamental interest in m...

11 Martin Emil Jakobsen, et al. ∙

research

∙ 06/01/2021

Invariant Policy Learning: A Causal Perspective

In the past decade, contextual bandit and reinforcement learning algorit...

0 Sorawit Saengkyongam, et al. ∙

research

∙ 05/22/2021

Statistical Testing under Distributional Shifts

Statistical hypothesis testing is a central problem in empirical inferen...

0 Nikolaj Thams, et al. ∙

research

∙ 03/03/2021

Regularizing towards Causal Invariance: Linear Models with Proxies

We propose a method for learning linear models whose predictive performa...

0 Michael Oberst, et al. ∙

research

∙ 01/18/2021

Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis

We study the problem of testing the null hypothesis that X and Y are con...

0 Anton Rask Lundborg, et al. ∙

research

∙ 06/12/2020

The Difficult Task of Distribution Generalization in Nonlinear Models

We consider the problem of predicting a response from a set of covariate...

0 Rune Christiansen, et al. ∙

research

∙ 05/18/2020

Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia

In many data scientific problems, we are interested not only in modeling...

0 Rune Christiansen, et al. ∙

research

∙ 05/07/2020

Distributional Robustness of K-class Estimators and the PULSE

In causal settings, such as instrumental variable settings, it is well k...

1 Martin Emil Jakobsen, et al. ∙

research

∙ 02/14/2020

Distributional robustness as a guiding principle for causality in cognitive neuroscience

While probabilistic models describe the dependence structure between obs...

0 Sebastian Weichwald, et al. ∙

research

∙ 01/17/2020

Causal models for dynamical systems

A probabilistic model describes a system in its observational state. In ...

0 Jonas Peters, et al. ∙

research

∙ 11/05/2019

Stabilizing Variable Selection and Regression

We consider regression in which one predicts a response Y with a set of ...

0 Niklas Pfister, et al. ∙

research

∙ 08/14/2019

Causal discovery in heavy-tailed models

Causal questions are omnipresent in many scientific problems. While much...

0 Nicola Gnecco, et al. ∙

research

∙ 10/28/2018

Identifying Causal Structure in Large-Scale Kinetic Systems

In the natural sciences, differential equations are widely used to descr...

0 Niklas Pfister, et al. ∙

research

∙ 08/16/2018

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 ...

0 Rune Christiansen, et al. ∙

research

∙ 04/19/2018

The Hardness of Conditional Independence Testing and the Generalised Covariance Measure

It is a common saying that testing for conditional independence, i.e., t...

0 Rajen D. Shah, et al. ∙

research

∙ 01/18/2018

Anchor regression: heterogeneous data meets causality

This is a preliminary draft of "Anchor regression: heterogeneous data me...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 11/18/2016

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

Structural causal models (SCMs), also known as non-parametric structural...

0 Stephan Bongers, et al. ∙

research

∙ 03/01/2016

Kernel-based Tests for Joint Independence

We investigate the problem of testing whether d random variables, which ...

0 Niklas Pfister, et al. ∙

research

∙ 06/08/2015

backShift: Learning causal cyclic graphs from unknown shift interventions

We propose a simple method to learn linear causal cyclic models in the p...

0 Dominik Rothenhäusler, et al. ∙

research

∙ 05/12/2015

Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to ...

0 Bernhard Schölkopf, et al. ∙

research

∙ 01/27/2015

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations

We describe a method to perform functional operations on probability dis...

0 Bernhard Schölkopf, et al. ∙

research

∙ 12/11/2014

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...

0 Joris M. Mooij, et al. ∙

research

∙ 03/03/2014

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

This work investigates the intersection property of conditional independ...

0 Jonas Peters, et al. ∙

research

∙ 10/06/2013

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

We develop estimation for potentially high-dimensional additive structur...

0 Peter Bühlmann, et al. ∙

research

∙ 09/26/2013

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

We propose a kernel method to identify finite mixtures of nonparametric ...

0 Eleni Sgouritsa, et al. ∙

research

∙ 09/26/2013

Causal Discovery with Continuous Additive Noise Models

We consider the problem of learning causal directed acyclic graphs from ...

0 Jonas Peters, et al. ∙

research

∙ 06/05/2013

Structural Intervention Distance (SID) for Evaluating Causal Graphs

Causal inference relies on the structure of a graph, often a directed ac...

0 Jonas Peters, et al. ∙

research

∙ 09/11/2012

Counterfactual Reasoning and Learning Systems

This work shows how to leverage causal inference to understand the behav...

0 Leon Bottou, et al. ∙

research

∙ 07/21/2012

Causal Inference on Time Series using Structural Equation Models

Causal inference uses observations to infer the causal structure of the ...

0 Jonas Peters, et al. ∙

research

∙ 06/27/2012

On Causal and Anticausal Learning

We consider the problem of function estimation in the case where an unde...

0 Bernhard Schoelkopf, et al. ∙

research

∙ 05/11/2012

Identifiability of Gaussian structural equation models with equal error variances

We consider structural equation models in which variables can be written...

0 Jonas Peters, et al. ∙

research

∙ 05/09/2012

Identifying confounders using additive noise models

We propose a method for inferring the existence of a latent common cause...

0 Dominik Janzing, et al. ∙

research

∙ 02/14/2012

Kernel-based Conditional Independence Test and Application in Causal Discovery

Conditional independence testing is an important problem, especially in ...

0 Kun Zhang, et al. ∙

research

∙ 02/14/2012

Identifiability of Causal Graphs using Functional Models

This work addresses the following question: Under what assumptions on th...

0 Jonas Peters, et al. ∙

research

∙ 02/14/2012

Detecting low-complexity unobserved causes

We describe a method that infers whether statistical dependences between...

0 Dominik Janzing, et al. ∙

research

∙ 12/12/2011

Robust Learning via Cause-Effect Models

We consider the problem of function estimation in the case where the dat...

0 Bernhard Schölkopf, et al. ∙

research

∙ 11/02/2009

Causal Inference on Discrete Data using Additive Noise Models

Inferring the causal structure of a set of random variables from a finit...

0 Jonas Peters, et al. ∙

Jonas Peters

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