9 citations · 33 across the 9 of their papers we have counts for
5 papers · 1 filter
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml +1
With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying…
Active Invariant Causal Prediction: Experiment Selection through Stability
Juan L. Gamella, Christina Heinze-Deml
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data origina…
Causal Structure Learning
Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen
Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that n…
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…