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

TL;DR

This paper discusses twelve key challenges in causal inference, emphasizing the importance of interdisciplinary approaches, domain knowledge, and future research directions to advance the field across various scientific domains.

Contribution

It outlines twelve major open problems in causality, highlighting the need for theoretical, methodological, and practical advancements for future progress.

Findings

Identification of key open problems in causal inference

Emphasis on interdisciplinary and domain-specific challenges

Call for integrated theoretical and practical solutions

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.