51 citations · 68 across the 10 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…
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki +2
We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimatio…
Causal analysis of Covid-19 spread in Germany
Atalanti A. Mastakouri, Bernhard Schölkopf
In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policie…
Quantifying intrinsic causal contributions via structure preserving interventions
Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri +3
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function…
Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance
Atalanti A. Mastakouri, Bernhard Schölkopf, Moritz Grosse-Wentrup
Transcranial alternating current stimulation (tACS) is becoming an important method in the field of motor rehabilitation because of its ability to non-invasively influence ongoing…