5 papers
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Armin Kekić, Sergio Hernan Garrido Mejia, Bernhard Schölkopf
Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our s…
Bayesian Hierarchical Invariant Prediction
Francisco Madaleno, Pernille Julie Viuff Sand, Francisco C. Pereira +1
We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (ICP) through the lens of Hierarchical Bayes. We leverage the hierarchical struct…
Causal vs. Anticausal merging of predictors
Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf +1
We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple…
Estimating Joint Interventional Distributions from Marginal Interventional Data
Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin Kekić +2
In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle. To this end, we ext…
Phenomenological Causality
Dominik Janzing, Sergio Hernan Garrido Mejia
Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables…