351 citations · 1.8k across the 37 of their papers we have counts for
46 papers
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…
Unfair Utilities and First Steps Towards Improving Them
Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters
Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, w…
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…
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald +1
Instrumental variable (IV) regression relies on instruments to infer causal effects from observational data with unobserved confounding. We consider IV regression in time series mo…
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 Ancestry Search
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response var…
Structure Learning for Directed Trees
Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann +1
Knowing the causal structure of a system is of fundamental interest in many areas of science and can aid the design of prediction algorithms that work well under manipulations to t…
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…
Regularizing towards Causal Invariance: Linear Models with Proxies
Michael Oberst, Nikolaj Thams, Jonas Peters +1
We propose a method for learning linear models whose predictive performance is robust to causal interventions on unobserved variables, when noisy proxies of those variables are ava…
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalise…
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,…
Distributional robustness of K-class estimators and the PULSE
Martin Emil Jakobsen, Jonas Peters
While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that…
Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
Sebastian Weichwald, Jonas Peters
While probabilistic models describe the dependence structure between observed variables, causal models go one step further: they predict, for example, how cognitive functions are a…
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…
Causal discovery in heavy-tailed models
Nicola Gnecco, Nicolai Meinshausen, Jonas Peters +1
Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are…
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…
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables
Rune Christiansen, Jonas Peters
Given a response and a vector of predictors, we investigate the problem of inferring direct causes of among the vector . Models for that…
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
Rajen D. Shah, Jonas Peters
It is a common saying that testing for conditional independence, i.e., testing whether whether two random vectors and are independent, given , is a hard statistical prob…
Anchor regression: heterogeneous data meets causality
Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann +1
We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal…
Invariant Causal Prediction for Nonlinear Models
Christina Heinze-Deml, Jonas Peters, Nicolai Meinshausen
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system's underlying causal structure…
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…