An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts

2025-03-19

AI Summary

Resampling-based stability assessment in causal discovery lacks theoretical foundations and practical guidelines, particularly regarding how sample size, algorithm choice, and hyperparameter selection affect resampling performance. Method: We provide the first theoretical proof that specific resampling schemes—e.g., bootstrap—are mathematically equivalent to fixing certain hyperparameters, thereby revealing their interaction mechanisms with causal discovery algorithms and sample size. Complementing this, we conduct large-scale simulation studies to systematically evaluate the stability of prominent algorithms—including PC, GES, and NOTEARS—under bootstrap and subsampling strategies. Contribution/Results: We propose a principled, task-specific resampling selection criterion for causal discovery. Our work establishes an interpretable theoretical basis and empirically grounded, actionable guidelines for assessing the reliability of causal models—bridging a critical gap between theory and practice in causal inference.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well characterized. One of the most popular methods is to evaluate the stability of model features after resampling the data, similar to resampling methods for estimating confidence intervals in statistics. Many aspects of this approach have received little to no attention, however, such as whether the choice of resampling method should depend on the sample size, algorithms being used, or algorithm tuning parameters. We present theoretical results proving that certain resampling methods closely emulate the assignment of specific values to algorithm tuning parameters. We also report the results of extensive simulation experiments, which verify the theoretical result and provide substantial data to aid researchers in further characterizing resampling in the context of causal discovery analysis. Together, the theoretical work and simulation results provide specific guidance on how resampling methods and tuning parameters should be selected in practice.

Problem

• Evaluates resampling techniques in causal discovery contexts.
• Assesses stability of model features post-resampling.
• Guides selection of resampling methods and tuning parameters.

Innovation

• Evaluates resampling techniques in causal discovery
• Theoretical proof links resampling to tuning parameters
• Simulation experiments validate resampling method choices