Methods and Tools for Causal Discovery and Causal Inference

Nowadays ML models are used in decision-making processes in real-world problems, by learning a function that maps the observed features with the decision outcomes. However these models usually do not convey causal information about the association in observational data, thus not being easily understandable for the average user, therefore not being possible to retrace the models’ steps, nor rely on its reasoning. Hence, it is natural to investigate more explainable methodologies, such as causal discovery approaches, since they apply processes that mimic human reasoning. For this reason, we propose the usage of such methodologies to create more explicable models that replicate human thinking, and that are easier for the average user to understand. This project's goal is to provide a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples.

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Author Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieiri, Dino Pedreschi and João Gama
Last Updated April 28, 2022, 10:28 (Europe/Lisbon)
Created April 28, 2022, 09:00 (Europe/Lisbon)
Citation Nogueira, A. R., Pugnana, A., Ruggieiri , S., Pedreschi, D., & Gama, J. (2022). Methods and Tools for Causal Discovery and Causal Inference [Data set]. INESC TEC.
DOI 10.25747/S9C3-AR89
dc.Created 23/01/2021
dc.Relation Nogueira, A. R., Pugnana, A., Ruggieri, S., Pedreschi, D., & Gama, J. (2022). Methods and tools for causal discovery and causal inference. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12( 2), e1449.
dc.Type Code and text explanations