CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
What this work does
A simulation-pretrained causal model conditions on observational and diverse intervention regimes to predict causal queries using intervention-aware representations.
Connection to discovery
Helps formalize discovery as narrowing an identified set, which can be useful even when the entire causal graph remains ambiguous.
Evidence
Inspected the distinction between singleton query identification, smaller compatible-model sets and prior-averaged posterior prediction error.
Scope and limitations
A Bayesian risk improvement under the stated prior is not a guarantee for every real SCM. Model assumptions, synthetic priors and experimental-regime compatibility remain essential.
THE RESEARCH CONVERSATION
Discussion & reactions
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