Causal Posterior Estimation
Causal Posterior Estimation (CPE) is a simulation-based inference method that improves posterior distribution approximation by explicitly modeling conditional dependencies from both prior and posterior programs. It uses normalizing flows with specialized architectures to hard-code these causal relationships, achieving highly accurate results and constant-time sampling for continuous distributions.