92 citations · 96 across the 5 of their papers we have counts for
7 papers
Towards Automated Causal Discovery: a case study on 5G telecommunication data
Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou +1
We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods.…
Using Causal Analysis for Conceptual Deep Learning Explanation
Sumedha Singla, Stephen Wallace, Sofia Triantafillou +1
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expe…
Causal Markov Boundaries
Sofia Triantafillou, Fattaneh Jabbari, Greg Cooper
Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model. In this paper, we focus on feature selection…
Learning Adjustment Sets from Observational and Limited Experimental Data
Sofia Triantafillou, Gregory Cooper
Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their in…
Reverse engineering neural networks from many partial recordings
Elahe Arani, Sofia Triantafillou, Konrad P. Kording
Much of neuroscience aims at reverse engineering the brain, but we only record a small number of neurons at a time. We do not currently know if reverse engineering the brain requir…
Rarely-switching linear bandits: optimization of causal effects for the real world
Benjamin Lansdell, Sofia Triantafillou, Konrad Kording
Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so…