Causality and Explainability in Temporal Data

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. More specifically, we suggest its application in methods such as decision trees and random forest, since by themselves are highly explainable correlation-based methods.
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Output

1 Conference Paper

1 Prototype

Dataset Repository

Project Partners:

• INESC TEC, Joao Gama
• Università di Pisa (UNIPI), Dino Pedreschi
• Consiglio Nazionale delle Ricerche (CNR), Fosca Giannotti

Primary Contact: Joao Gama, INESC TEC, University of Porto

Main results of micro project:

1) Journal paper submitted to WiRES – data mining and knowledge discovery:
Methods and Tools for Causal Discovery and Causal Inference
Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama
(under evaluation)

2) Github repository of datasets, software, and papers related to causal discovery and causal inference research

https://github.com/AnaRitaNogueira/Methods-and-Tools-for-Causal-Discovery-and-Causal-Inference

Contribution to the objectives of HumaneAI-net WPs

The HumanE-AI project thinks a society of increasing interactions between humans and artificial agents. All around the project, causal models are relevant for plausible models of human behavior, man-machine explanations, and upgrading machine-learning algorithms with causal-inference mechanisms.

The output of the micro-project presents a deep study about causal discovery and causal inference. Moreover, the github repository of datasets, papers, and code will be an excellent source of resources for those want to study the topic.

Tangible outputs

• Publication: Methods and Tools for Causal Discovery and Causal Inference – Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama
• Dataset: repository of datasets, software, and papers related to causal discovery and causal inference research – Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama
  https://github.com/AnaRitaNogueira/Methods-and-Tools-for-Causal-Discovery-and-Causal-Inference