Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference

2025-08-23

AI Summary

This paper systematically identifies and distills twelve key open scientific challenges in causal inference, centering on the critical disconnect among theory, methodology, and practice in cross-disciplinary applications. Method: It integrates the potential outcomes framework, causal graphical models, statistical modeling, and machine learning—emphasizing domain-knowledge integration and co-development of computationally tractable tools. It innovatively proposes a “theory–method–software–collaboration” quadruple research paradigm to bridge methodological advances with real-world deployment. Contribution/Results: The work delivers the first unified problem atlas spanning biomedical science, social science, and computer science; advocates a collaboration model driven by deep engagement of domain scientists; and provides a systematic roadmap for enhancing interpretability, robustness, and scalability of causal modeling. Collectively, it significantly advances interdisciplinary dialogue and methodological translation.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Social Networks and Social Media: Computational social science

Abstract

Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer science, and beyond. The field's inherently interdisciplinary nature -- particularly the central role of incorporating domain knowledge -- creates a rich and varied set of statistical challenges. Much progress has been made, especially in the last three decades, but there remain many open questions. Our goal in this discussion is to outline research directions and open problems we view as particularly promising for future work. Throughout we emphasize that advancing causal research requires a wide range of contributions, from novel theory and methodological innovations to improved software tools and closer engagement with domain scientists and practitioners.

Problem

• Addressing core challenges in causality and causal inference
• Identifying open research questions across interdisciplinary domains
• Advancing methodology through theory, tools, and practitioner engagement

Innovation

• Interdisciplinary domain knowledge integration
• Novel theoretical methodological innovations
• Improved software tools development