ShapleyPipe: Hierarchical Shapley Search for Data Preparation Pipeline Construction

TL;DR

ShapleyPipe introduces a hierarchical, game-theoretic approach to automate and interpret data pipeline construction, significantly reducing search complexity and improving performance over existing methods.

Contribution

It presents a novel hierarchical framework using Shapley values for interpretable, efficient data pipeline search, with new mechanisms for tractable Shapley computation.

Findings

Achieves 98.1% of high-budget baseline performance with 24% fewer evaluations.

Outperforms state-of-the-art reinforcement learning methods by 3.6%.

Provides highly interpretable operator valuations with 0.933 correlation to empirical performance.

Abstract

Automated data preparation pipeline construction is critical for machine learning success, yet existing methods suffer from two fundamental limitations: they treat pipeline construction as black-box optimization without quantifying individual operator contributions, and they struggle with the combinatorial explosion of the search space ($N^M$ configurations for N operators and pipeline length M). We introduce ShapleyPipe, a principled framework that leverages game-theoretic Shapley values to systematically quantify each operator's marginal contribution while maintaining full interpretability. Our key innovation is a hierarchical decomposition that separates category-level structure search from operator-level refinement, reducing the search complexity from exponential to polynomial. To make Shapley computation tractable, we develop: (1) a Multi-Armed Bandit mechanism for intelligent category evaluation with provable convergence guarantees, and (2) Permutation Shapley values to correctly capture position-dependent operator interactions. Extensive evaluation on 18 diverse datasets demonstrates that ShapleyPipe achieves 98.1\% of high-budget baseline performance while using 24\% fewer evaluations, and outperforms the state-of-the-art reinforcement learning method by 3.6\%. Beyond performance gains, ShapleyPipe provides interpretable operator valuations ($\rho$=0.933 correlation with empirical performance) that enable data-driven pipeline analysis and systematic operator library refinement.