PRIM meta-learns a Model-Averaged Causal Estimation transformer to perform Bayesian RCA by marginalizing structural uncertainty over synthetic causal priors, achieving 17ms inference on systems up to 100 variables.
Root cause discovery via permutations and cholesky decomposition.Journal of the Royal Statistical Society Series B: Statistical Methodology, page qkaf066
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PRIM: Meta-Learned Bayesian Root Cause Analysis
PRIM meta-learns a Model-Averaged Causal Estimation transformer to perform Bayesian RCA by marginalizing structural uncertainty over synthetic causal priors, achieving 17ms inference on systems up to 100 variables.