ergodic 0.1.0

First public release of ergodic, the Causal AI library from ergodic.ai.

Pillars

• Graph & knowledge: one mixed-graph core (DAG, ADMG, MAG, CPDAG, PAG) with real validators (Meek, FCI closure, MAG/PAG round trips), plus composable domain knowledge.
• Identification: adjustment, front-door, instruments, the complete Shpitser-Pearl ID/IDC algorithm with typed estimands, generalized adjustment on equivalence classes, IDA, and mediation (NDE/NIE/CDE).
• Inference: estimate_effect over thirteen estimators (AIPW, DML, DR/R/X/S/T-learners, ...), per-unit CATE with honest explainability (BLP, GATES, RATE, policy trees, causal forest), Bayesian learners, and the quasi-experimental designs (DiD incl. staggered, synthetic control, ITS, event studies).
• Discovery: PC, FCI, RFCI, GES, BOSS, GRaSP, A*, LiNGAM, glasso, MMPC, PCMCI, Granger, with typed CI tests and scores, knowledge in four modes, and a moving-block bootstrap.
• Process intelligence: process maps, case tables that feed the causal bridge, Inductive Miner to sound workflow nets, token replay and alignments, decision-point analysis with positivity.
• SCM: Bayesian structural causal models on PyMC with pm.do interventions, twin-network counterfactuals, per-unit ITE, latent-district ADMG fitting, and an identification-gated honesty layer.
• Forecast: Bayesian structural forecasting (forecast/backtest) with temporal mechanisms, hierarchy reconciliation (bottom-up/top-down/middle-out/MinT/trust), window-graph drivers with staged posterior propagation, scenarios, and attribution.

Install: pip install ergodic (add [forecast], [scm], or [bayes] for the PyMC-backed pillars; jax+numpyro for the fast sampler).