38 citations · 62 across the 16 of their papers we have counts for
19 papers
Invariance-Based Dynamic Regret Minimization
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
We consider stochastic non-stationary linear bandits where the linear parameter connecting contexts to the reward changes over time. Existing algorithms in this setting localize th…
Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
Felix Schur, Niklas Pfister, Peng Ding +2
We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates and an outcome under different…
Invariant Subspace Decomposition
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
We consider the task of predicting a response Y from a set of covariates X in settings where the conditional distribution of Y given X changes over time. For this to be feasible, a…
Causal Change Point Detection and Localization
Shimeng Huang, Jonas Peters, Niklas Pfister
Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean,…
Boosted Control Functions: Distribution generalization and invariance in confounded models
Nicola Gnecco, Jonas Peters, Sebastian Engelke +1
Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However…
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar +2
The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent…
Effect-Invariant Mechanisms for Policy Generalization
Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja +2
Policy learning is an important component of many real-world learning systems. A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks. Rec…
Identifiability of Sparse Causal Effects using Instrumental Variables
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…
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
Exploiting Independent Instruments: Identification and Distribution Generalization
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister +1
Instrumental variable models allow us to identify a causal function between covariates and a response , even in the presence of unobserved confounding. Most of the existing…
Invariant Policy Learning: A Causal Perspective
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters +1
Contextual bandit and reinforcement learning algorithms have been successfully used in various interactive learning systems such as online advertising, recommender systems, and dyn…
Statistical Testing under Distributional Shifts
Nikolaj Thams, Sorawit Saengkyongam, Niklas Pfister +1
In this work, we introduce statistical testing under distributional shifts. We are interested in the hypothesis for a target distribution , but observe data from…
A causal framework for distribution generalization
Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen +2
We consider the problem of predicting a response from a set of covariates when test and training distributions differ. Since such differences may have causal explanations,…
Causal models for dynamical systems
Jonas Peters, Stefan Bauer, Niklas Pfister
A probabilistic model describes a system in its observational state. In many situations, however, we are interested in the system's response under interventions. The class of struc…
Stabilizing Variable Selection and Regression
Niklas Pfister, Evan G. Williams, Jonas Peters +2
We consider regression in which one predicts a response with a set of predictors across different experiments or environments. This is a common setup in many data-driven sc…
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Niklas Pfister, Stefan Bauer, Jonas Peters
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference…
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
Niklas Pfister, Sebastian Weichwald, Peter Bühlmann +1
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components…
Invariant Causal Prediction for Sequential Data
Niklas Pfister, Peter Bühlmann, Jonas Peters
We investigate the problem of inferring the causal predictors of a response from a set of explanatory variables . Classical ordinary least squares regressi…
Kernel-based Tests for Joint Independence
Niklas Pfister, Peter Bühlmann, Bernhard Schölkopf +1
We investigate the problem of testing whether random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two…