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

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).

Abstract
Determining the cause of a particular event has been a case of study for several researchers over the years. Finding out why an event happens (its cause) means that, for example, if we remove the cause from the equation, we can stop the effect from happening or if we replicate it, we can create the subsequent effect. Causality can be seen as a mean of predicting the future, based on information about past events, and with that, prevent or alter future outcomes. This temporal notion of past and future is often one of the critical points in discovering the causes of a given event. The purpose of this survey is to present a cross-sectional view of causal discovery domain, with an emphasis in the machine learning/data mining area.

Abstract
One of the most significant challenges for machine learning nowadays is the discovery of causal relationships from data. This causal discovery is commonly performed using Bayesian like algorithms. However, more recently, more and more causal discovery algorithms have appeared that do not fall into this category. In this paper, we present a new algorithm that explores global causal association rules with Uncertainty Coefficient. Our algorithm, CRPA-UC, is a global structure discovery approach that combines the advantages of association mining with causal discovery and can be applied to binary and non-binary discrete data. This approach was compared to the PC algorithm using several well-known data sets, using several metrics.

Abstract
This work aims to develop a Machine Learning framework to predict voting behaviour. Data resulted from longitudinally collected variables during the Portuguese 2019 general election campaign. Naive Bayes (NB), and Tree Augmented Naive Bayes (TAN) and three different expert models using Dynamic Bayesian Networks (DBN) predict voting behaviour systematically for each moment in time considered using past information. Even though the differences found in some performance comparisons are not statistically significant, TAN and NB outperformed DBN experts' models. The learned models outperformed one of the experts' models when predicting abstention and two when predicting right-wing parties vote. Specifically, for the right-wing parties vote, TAN and NB presented satisfactory accuracy, while the experts' models were below 50% in the third evaluation moment.