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Towards Automated Causal Discovery: a case study on 5G telecommunication data
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
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare.
Causal Markov Boundaries
Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model.
Learning Adjustment Sets from Observational and Limited Experimental Data
In this work we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects.
Rarely-switching linear bandits: optimization of causal effects for the real world
Using this idea, and the theory of linear contextual bandits, we present a conservative policy updating procedure which updates a deterministic policy only when justified.
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
In this work, we present algorithm COmbINE, which accepts a collection of data sets over overlapping variable sets under different experimental conditions; COmbINE then outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets.
