51 citations · 53 across the 7 of their papers we have counts for
10 papers
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…
Tightening Bounds on Probabilities of Causation By Merging Datasets
Numair Sani, Atalanti A. Mastakouri
Probabilities of Causation (PoC) play a fundamental role in decision-making in law, health care and public policy. Nevertheless, their point identification is challenging, requirin…
Beyond Single-Feature Importance with ICECREAM
Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri +1
Which set of features was responsible for a certain output of a machine learning model? Which components caused the failure of a cloud computing application? These are just two exa…
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri +2
As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconcepti…
Toward Falsifying Causal Graphs Using a Permutation-Based Test
Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum +2
Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is…
Bounding probabilities of causation through the causal marginal problem
Numair Sani, Atalanti A. Mastakouri, Dominik Janzing
Probabilities of Causation play a fundamental role in decision making in law, health care and public policy. Nevertheless, their point identification is challenging, requiring stro…