Domain-Agnostic Causal Simulation Engine with Mixed Variable Types

A domain-agnostic simulation engine for structural causal models with heterogeneous variable types. Users define systems of continuous, categorical, and ordinal features, declare a directed acyclic graph (DAG) over them, attach typed generative mechanisms to each node, and simulate synthetic populations. A pre-built mechanism library covers all nine parent-type/child-type combinations. Vine copula mechanisms estimate full joint dependence from observed data via pair-copula constructions (Bedford and Cooke, 2002) . Individual-level counterfactual queries are supported through partial propagation, which freezes unaffected random draws and resamples only the downstream subgraph, enabling individual treatment effect (ITE) estimation alongside population-level average treatment effects (ATE). Any pre-fitted predictive model can serve as a generative mechanism through three wrapper functions. Systems serialise to JSON for cross-language portability with the companion Python package.