JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertainty

TL;DR

JANUS is a novel framework that unifies causal modeling and probabilistic sampling to generate high-fidelity synthetic data with guaranteed constraints and fast uncertainty estimation.

Contribution

It introduces Reverse-Topological Back-filling and an Analytical Uncertainty Decomposition, enabling constraint satisfaction and rapid uncertainty estimation without rejection sampling.

Findings

Achieves 100% constraint satisfaction on feasible sets.

Provides 128x faster uncertainty estimation than Monte Carlo methods.

Outperforms baselines in fidelity and constraint handling across multiple datasets.

Abstract

High-stakes synthetic data generation faces a fundamental Quadrilemma: achieving Fidelity to the original distribution, Control over complex logical constraints, Reliability in uncertainty estimation, and Efficiency in computational cost -- simultaneously. State-of-the-art Deep Generative Models (CTGAN, TabDDPM) excel at fidelity but rely on inefficient rejection sampling for continuous range constraints. Conversely, Structural Causal Models offer logical control but struggle with high-dimensional fidelity and complex noise inversion. We introduce JANUS (Joint Ancestral Network for Uncertainty and Synthesis), a framework that unifies these capabilities using a DAG of Bayesian Decision Trees. Our key innovation is Reverse-Topological Back-filling, an algorithm that propagates constraints backwards through the causal graph, achieving 100% constraint satisfaction on feasible constraint sets without rejection sampling. This is paired with an Analytical Uncertainty Decomposition derived from Dirichlet priors, enabling 128x faster uncertainty estimation than Monte Carlo methods. Across 15 datasets and 523 constrained scenarios, JANUS achieves state-of-the-art fidelity (Detection Score 0.497), eliminates mode collapse on imbalanced data, and provides exact handling of complex inter-column constraints (e.g., Salary_offered >= Salary_requested) where baselines fail entirely.