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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A note on the interpretation of tree-based regression models.

Anna Gottard1,2, Giulia Vannucci1, Giovanni Maria Marchetti1,2

  • 1Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.

Biometrical Journal. Biometrische Zeitschrift
|May 26, 2020
PubMed
Summary
This summary is machine-generated.

Tree-based models may incorrectly identify important variables. These models can overlook direct causes, favoring background variables with indirect effects, hindering accurate causal inference and segmentation.

Keywords:
interpretable machine learningmarginal and conditional dependenceunderlying explanatory processvariable importancevariable selection bias

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Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Tree-based models are widely used for prediction and variable importance analysis.
  • Their application in understanding complex relationships, including causal inference, is growing.

Purpose of the Study:

  • To investigate the reliability of variable importance measures in tree-based models when dealing with chained direct and indirect effects.
  • To identify potential pitfalls in variable selection within these models.

Main Methods:

  • Analysis of variable importance measures in tree-based algorithms.
  • Simulation or theoretical examination of models with direct and indirect causal pathways.

Main Results:

  • Typical variable importance measures in tree-based models tend to favor background variables with strong indirect effects.
  • Directly influential variables are often overlooked due to the greedy nature of the algorithm's variable selection process.
  • This bias is particularly evident when the underlying data-generating process involves chains of effects.

Conclusions:

  • Standard variable importance metrics in tree-based models can be misleading when causal chains are present.
  • The findings highlight a significant pitfall for applications relying on these models for causal inference, population segmentation, and understanding data-generating processes.
  • Alternative or adjusted methods may be necessary for accurate variable identification in complex causal structures.