Foundation Models for Partial Causal Identification
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Abstract (arXiv)
This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data. We show that a canonical prior can be defined with full support over the space of structural causal models with discrete observables. With this canonical prior, we translate the problem of bounding counterfactuals into that of learning distributions over functions that map data (and possibly structural assumptions) to a causal query of interest. This extends the promising causal foundational modelling paradigm to the estimation of partially-identifiable causal effects, i.e., under unobserved confounding, where multiple values are equally compatible with the observed data and prior structural assumptions.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 21 Aug 2026.
Builds on
20 of 40 references.
References · 20
- 2021Transformers Can Do Bayesian Inferencecited by 360
- 2018Neural Processescited by 311
- 2013Probabilities of Causation: Bounds and Identificationcited by 90
- 2013Counterfactual Probabilities: Computational Methods, Bounds and Applicationscited by 67
- 2021Partial Counterfactual Identification from Observational and Experimental Datacited by 64
- 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 51
- 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 49
- 2021An Automated Approach to Causal Inference in Discrete Settingscited by 42
- 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 32
- 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25
- 2020A General Method for Deriving Tight Symbolic Bounds on Causal Effectscited by 18
- 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
8 more
- 2025Black Box Causal Inference: Effect Estimation via Meta Predictioncited by 13
- 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcited by 13
- 2020Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactualscited by 8
- 2023Towards Bounding Causal Effects under Markov Equivalencecited by 8
- 2025Confounding Robust Deep Reinforcement Learning: A Causal Approachcited by 7
- 2026Use What You Know: Causal Foundation Models with Partial Graphscited by 7
- 2026Deriving Complete Constraints in Hidden Variable Modelscited by 2
- 2026Information-Theoretic Causal Bounds under Unmeasured Confoundingcited by 2
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Citing papers per year: 2026: 2.
2 papers.