Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
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.
Cite
@article{arxiv.2508.17099,
title = {Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference},
author = {Carlos Cinelli and Avi Feller and Guido Imbens and Edward Kennedy and Sara Magliacane and Jose Zubizarreta},
journal= {arXiv preprint arXiv:2508.17099},
year = {2025}
}