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Semi-causal decision trees


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).

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    “For each of the separate levels of the co-variable set h = 1, 2, ..., q, the response variable is distributed at random with respect to the sub-populations, i.e. the data in the respective rows of the hth table can be regarded as a successive set of simple random samples of sizes {Nhi.} from a fixed population corresponding to the marginal total distribution of the response variable {Nh.j}” [10].

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    We used the WEKA jar file provided kindly by the authors to compare with our methodology.

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    We used the WEKA implementation.

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This research was carried out in the context of the project FailStopper (DSAIPA/DS/0086/2018) and supported by the Fundação para a Ciência e Tecnologia (FCT), Portugal for the PhD Grant SFRH/BD/146197/2019.

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Correspondence to Ana Rita Nogueira.

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Nogueira, A.R., Ferreira, C.A. & Gama, J. Semi-causal decision trees. Prog Artif Intell (2021).

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  • Causal discovery
  • Correlation
  • Decision tree
  • Semi-causal information gain
  • Semi-causal gain ratio
  • Uncertainty coefficient