1 citations · 1 across the 3 of their papers we have counts for
5 papers
Estimating Joint Interventional Distributions from Marginal Interventional Data
Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin KekiÄ +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…
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…
Root Cause Analysis of Outliers with Missing Structural Knowledge
William Roy Orchard, Nastaran Okati, Sergio Hernan Garrido Mejia +2
The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting f…
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Armin KekiÄ, 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…
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…