Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping

2026-01-23

AI Summary

This study addresses the challenge posed by high-dimensional and highly correlated dietary intake data in causal inference, a key concern in nutritional epidemiology where interest lies in latent dietary patterns and their heterogeneous effects on health outcomes. To this end, the authors propose an innovative three-level ordinal exposure mapping framework tailored for high-dimensional nutritional data, integrating factor analysis with Bayesian nonparametric causal inference. They extend Bayesian Causal Forests to estimate heterogeneous causal effects across multiple dietary patterns. In both simulation studies and multicenter cohort analyses, the approach successfully identified six distinct dietary patterns, revealing that plant lipid–antioxidant, plant-based, animal protein, and dairy patterns are significantly associated with lower body mass index and fasting insulin levels.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies. However, causal inference in nutritional epidemiology faces several challenges: (i) high-dimensional and correlated food/nutrient intake data induce massive treatment levels; (ii) nutritional studies are interested in latent dietary patterns rather than single food items; and (iii) the goal is to estimate heterogeneous causal effects of these dietary patterns on health outcomes. We address these challenges by introducing a sophisticated exposure mapping framework that reduces the high-dimensional treatment space via factor analysis and enables the identification of dietary patterns. We also extend the Bayesian Causal Forest to accommodate three ordered levels of dietary exposure, better capturing the complex structure of nutritional data and enabling estimation of heterogeneous causal effects. We evaluate the proposed method through extensive simulations and apply it to a multi-center epidemiological study of Hispanic/Latino adults residing in the US. Using high-dimensional dietary data, we identify six dietary patterns and estimate their causal link with two key health risk factors: body mass index and fasting insulin levels. Our findings suggest that higher consumption of plant lipid-antioxidant, plant-based, animal protein, and dairy product patterns is associated with reduced risk.

Problem

• causal inference
• high-dimensional data
• dietary patterns
• heterogeneous treatment effects
• nutritional epidemiology

Innovation

• Bayesian Nonparametrics
• Factor-Based Exposure Mapping
• High-Dimensional Causal Inference
• Dietary Patterns
• Heterogeneous Treatment Effects