CausalWrap: 用于表格合成数据的模型无关因果约束包装器
CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
AI总结：
CausalWrap通过注入部分因果知识提升表格合成数据的因果忠实度，减少治疗效应误差，提升因果推理性能。
摘要：
表格合成数据生成器通常被训练以匹配观测分布，这可以产生高传统效用（例如列相关性、预测准确性），但会损害与因果分析和离分布（OOD）推理相关的结构性关系。当合成数据的下游应用涉及因果推理——估计治疗效应、评估政策或测试中介路径时，仅仅匹配观测分布是不够的：结构性忠实和治疗机制的保存变得至关重要。我们提出了CausalWrap（CW），一种模型无关的包装器，它将部分因果知识（PCK）——受信任的边、禁止的边以及定性/单调约束——注入任何预训练的基生成器（GAN、VAE或扩散模型）中，而无需访问其内部结构。CW学习一个轻量级、可微的后处理修正映射，应用于基生成器的样本，通过在增强拉格朗日调度下优化因果惩罚项。我们提供了将基于惩罚的优化与约束满足联系起来的理论结果，并将近似因子分解与联合分布控制联系起来。我们在具有已知地面真实干预的模拟结构性因果模型（SCMs）上验证了CW，半合成因果基准（IHDP和ACIC风格套件）以及具有专家提取部分图的现实世界ICU队列（MIMIC-IV）。CW在多样化的基生成器上提高了因果忠实度——例如，在ACIC上将平均治疗效应（ATE）误差减少高达63%，在重症监护（ICU）队列上将ATE一致度从0.00提升到0.38，同时大致保留传统效用。
英文摘要：
Tabular synthetic data generators are typically trained to match observational distributions, which can yield high conventional utility (e.g., column correlations, predictive accuracy) yet poor preservation of structural relations relevant to causal analysis and out-of-distribution (OOD) reasoning. When the downstream use of synthetic data involves causal reasoning -- estimating treatment effects, evaluating policies, or testing mediation pathways -- merely matching the observational distribution is insufficient: structural fidelity and treatment-mechanism preservation become essential. We propose CausalWrap (CW), a model-agnostic wrapper that injects partial causal knowledge (PCK) -- trusted edges, forbidden edges, and qualitative/monotonic constraints -- into any pretrained base generator (GAN, VAE, or diffusion model), without requiring access to its internals. CW learns a lightweight, differentiable post-hoc correction map applied to samples from the base generator, optimized with causal penalty terms under an augmented-Lagrangian schedule. We provide theoretical results connecting penalty-based optimization to constraint satisfaction and relating approximate factorization to joint distributional control. We validate CW on simulated structural causal models (SCMs) with known ground-truth interventions, semi-synthetic causal benchmarks (IHDP and an ACIC-style suite), and a real-world ICU cohort (MIMIC-IV) with expert-elicited partial graphs. CW improves causal fidelity across diverse base generators -- e.g., reducing average treatment effect (ATE) error by up to 63% on ACIC and lifting ATE agreement from 0.00 to 0.38 on the intensive care unit (ICU) cohort -- while largely retaining conventional utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
