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About

About

Born in the 90s, I completed my academic course at the Instituto de Engenharia da Universidade do Porto (ISEP) with a Master's degree in Computer Systems.

I am currently working on a research grant at LIAAD in the NanoSTIMA project where I am developing methods to accurately predict changes in the state of a patient with kidney disease.

Interest
Topics
Details

Details

  • Name

    Ana Rita Nogueira
  • Cluster

    Computer Science
  • Role

    External Student
  • Since

    01st October 2016
002
Publications

2022

Methods and tools for causal discovery and causal inference

Authors
Nogueira, AR; Pugnana, A; Ruggieri, S; Pedreschi, D; Gama, J;

Publication
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

2022

Semi-causal decision trees

Authors
Nogueira, AR; Ferreira, CA; Gama, J;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).

2021

Causal discovery in machine learning: Theories and applications

Authors
Nogueira, AR; Gama, J; Ferreira, CA;

Publication
JOURNAL OF DYNAMICS AND GAMES

Abstract

2021

Generalised Partial Association in Causal Rules Discovery

Authors
Nogueira, AR; Ferreira, C; Gama, J; Pinto, A;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021)

Abstract

2021

Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks

Authors
Costa, P; Nogueira, AR; Gama, J;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021)

Abstract