351 citations · 1.8k across the 37 of their papers we have counts for
5 papers · 1 filter
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…