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

arXiv: https://arxiv.org/abs/2508.17099 · HTML: https://mlsanity.com/paper/2508.17099/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2025.

## Built on by (most cited first, 9 of 9)

- [IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning](https://mlsanity.com/paper/2605.12924/) (2026)
- [Causal Evaluation of Membership Inference Attacks](https://mlsanity.com/paper/2602.02819/) (2026)
- [Toward an Asymptotic Efficiency Theory on Regular Parameter Manifolds](https://mlsanity.com/paper/2510.13703/) (2025)
- [Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Framework](https://mlsanity.com/paper/2602.07613/) (2026)
- [Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression](https://mlsanity.com/paper/2602.18958/) (2026)
- [Which Alert Removals are Beneficial?](https://mlsanity.com/paper/2603.21322/) (2026)
- [Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes](https://mlsanity.com/paper/2603.27114/) (2026)
- [InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI](https://mlsanity.com/paper/2604.04274/) (2026)
- [Bayesian Mediation Analysis for Individualized Treatment Rules](https://mlsanity.com/paper/2607.28804/) (2026)

## Abstract (arXiv)

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.