One year ago, NeurIPS published the first papers introducing causal foundation models (CFMs) [1, 2] – pre-trained transformers that predict causal effects from observational data using only in-context learning [3]. Now, CFMs have been tested and deployed on real-world prediction problems at TD, one of North America's largest banks. This represents a paradigm shift in how industry will use models to set prices, personalize offers, and communicate with customers. In this talk we dive into where causal modeling is used today across industries, and how that work will change to harness CFMs. We give researchers a crash course in how CFMs can generalize to unseen causal tasks, and outline the considerations that practitioners will need to account for when replacing legacy causal models, using examples from TD's practical experience with the technology. We compare the trajectory of CFM development with that of tabular foundation models and LLMs to paint a path for future improvements. As CFMs are a new technology, there remain many open avenues for improvement and inquiry. To close we present a large-scale research program, from benchmarking, to training, evaluation, capability, and fundamental understanding, and call researchers at NeurIPS to tackle these open problems.

To be presented by Jesse Cresswell.

[1] Balazadeh, Kamkari, Thomas, Ma, Li, Cresswell, Krishnan. "CausalPFN: Amortized Causal Effect Estimation via In-Context Learning" NeurIPS 2026
[2] Robertson, Reuter, Guo, Hollmann, Hutter, Schölkopf. "Do-PFN: In-Context Learning for Causal Effect Estimation" NeurIPS 2026
[3] Stith, Rahmani, Cresswell. "Causal Foundation Models" arXiv: 2609.03003