6 citations · 9 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Model-based causal feature selection for general response types
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg +2
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal featur…