Search Results for author: Niklas Pfister

Found 11 papers, 7 papers with code

Supervised Learning and Model Analysis with Compositional Data

1 code implementation15 May 2022 Shimeng Huang, Elisabeth Ailer, Niki Kilbertus, Niklas Pfister

We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data.

Identifiability of Sparse Causal Effects using Instrumental Variables

no code implementations17 Mar 2022 Niklas Pfister, Jonas Peters

Exogenous heterogeneity, for example, in the form of instrumental variables can help us learn a system's underlying causal structure and predict the outcome of unseen intervention experiments.

Exploiting Independent Instruments: Identification and Distribution Generalization

1 code implementation3 Feb 2022 Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters

Most of the existing estimators assume that the error term in the response Y and the hidden confounders are uncorrelated with the instruments Z.


Invariant Policy Learning: A Causal Perspective

1 code implementation1 Jun 2021 Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister

We view the environmental shift problem through the lens of causality and propose multi-environment contextual bandits that allow for changes in the underlying mechanisms.

Multi-Armed Bandits Recommendation Systems

A causal framework for distribution generalization

1 code implementation12 Jun 2020 Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters

We introduce the formal framework of distribution generalization that allows us to analyze the above problem in partially observed nonlinear models for both direct interventions on $X$ and interventions that occur indirectly via exogenous variables $A$.

Methodology Primary 62Gxx, secondary 62G35, 62G08, 62D20

Causal models for dynamical systems

no code implementations17 Jan 2020 Jonas Peters, Stefan Bauer, Niklas Pfister

In this chapter, we provide a natural and straight-forward extension of this concept to dynamical systems, focusing on continuous time models.

Methodology Dynamical Systems

Stabilizing Variable Selection and Regression

1 code implementation5 Nov 2019 Niklas Pfister, Evan G. Williams, Jonas Peters, Ruedi Aebersold, Peter Bühlmann

In particular, it is useful to distinguish between stable and unstable predictors, i. e., predictors which have a fixed or a changing functional dependence on the response, respectively.

Methodology Applications

Learning stable and predictive structures in kinetic systems: Benefits of a causal approach

no code implementations28 Oct 2018 Niklas Pfister, Stefan Bauer, Jonas Peters

Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective.

Causal Inference Model Selection

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

3 code implementations4 Jun 2018 Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.

Causal Inference EEG

Kernel-based Tests for Joint Independence

no code implementations1 Mar 2016 Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf, Jonas Peters

Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation.

Causal Discovery

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