6 papers
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…
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…
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…
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…
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…