Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation

Wan, Ke; Tanioka, Kensuke; Shimokawa, Toshio

doi:10.1002/sim.9812

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…It also enables an initial understanding of the operational mechanism of the intelligent model through the analysis of principal rules (Friedman and Popescu, 2008). Specifically, given a marine data sample x = {x 1 ,x 2 ,…,x n } T ∈ R n , the RuleFit model is defined as follows (Wan et al, 2023) (Equation 4):…”

Section: Model Computationmentioning

confidence: 99%

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

Li,

Liu,

Yang

et al. 2024

Front. Mar. Sci.

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Variations in Marine Dissolved Oxygen Concentrations (MDOC) play a critical role in the study of marine ecosystems and global climate evolution. Although artificial intelligence methods, represented by deep learning, can enhance the precision of MDOC inversion, the uninterpretability of the operational mechanism involved in the “black-box” often make the process difficult to interpret. To address this issue, this paper proposes a high-precision interpretable framework (CDRP) for intelligent MDOC inversion, including Causal Discovery, Drift Detection, RuleFit Model, and Post Hoc Analysis. The entire process of the proposed framework is fully interpretable: (i) The causal relationships between various elements are further clarified. (ii) During the phase of concept drift analysis, the potential factors contributing to changes in marine data are extracted. (iii) The operational rules of RuleFit ensure computational transparency. (iv) Post hoc analysis provides a quantitative interpretation from both global and local perspectives. Furthermore, we have derived quantitative conclusions about the impacts of various marine elements, and our analysis maintains consistency with conclusions in marine literature on MDOC. Meanwhile, CDRP also ensures the precision of MDOC inversion: (i) PCMCI causal discovery eliminates the interference of weakly associated elements. (ii) Concept drift detection takes more representative key frames. (iii) RuleFit achieves higher precision than other models. Experiments demonstrate that CDRP has reached the optimal level in single point buoy data inversion task. Overall, CDRP can enhance the interpretability of the intelligent MDOC inversion process while ensuring high precision.

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Section: Model Computationmentioning

confidence: 99%

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

Li,

Liu,

Yang

et al. 2024

Front. Mar. Sci.

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“…To arrive at a more effective solution strategy, it is advisable to employ multiple commonly used variable selection methods separately and integrate the common factors among these methods. Notably, some scholars have expanded the concept of ensemble learning to include group variable selection algorithms (Wang [ 34 ], 2022; Wan and Tanioka [ 35 ], 2023; Hussein and Rahul [ 36 ], 2023). To accommodate more complex group structures, Thompson (2021) [ 37 ] proposed a sparse estimator of group structure by combining group subset selection and shrinkage.…”

Section: Introductionmentioning

confidence: 99%

A penalized variable selection ensemble algorithm for high-dimensional group-structured data

Li,

Pan,

Zhao

et al. 2024

PLoS ONE

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This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy.

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“…Wan et al. 17 proposed a RuleFit-based method to estimate the HTE; however, it does not consider the prognostic effect. By contrast, the proposed method considers the prognostic effect in estimating HTE, thereby allowing for a more refined interpretation of the treatment effect.…”

Section: Introductionmentioning

confidence: 99%

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects

Hiraishi,

Wan,

Tanioka

et al. 2024

Stat Methods Med Res

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We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.

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Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation

Cited by 5 publications

References 52 publications

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

A penalized variable selection ensemble algorithm for high-dimensional group-structured data

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects

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