lepidoptera: 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) <doi:10.1214/aos/1031689014>. 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.
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