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Path Dependent Structural Equation Models
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time.
A Semiparametric Approach to Interpretable Machine Learning
In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods.
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