面向可逆潜在因果模型的域反事实表征研究
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
AI总结：
该研究针对潜在因果变量不可观测场景下的反事实估计难题，聚焦域反事实查询，通过假设干预可逆性与稀疏性推导误差界，提出带约束的生成式估计算法，在模拟与图像实验中优于基线方法。
摘要：
回答反事实查询在可解释性、鲁棒性和公平性等方面具有重要应用，但当因果变量不可观测、且观测结果是这些潜在变量的非线性混合（如图像像素）时，该任务极具挑战性。一种方法是恢复潜在结构因果模型（SCM），但由于需要强假设（例如因果机制的线性性或完美的原子干预），这在实践中可能难以实现。同时，更实用的基于机器学习的方法使用朴素域迁移模型生成反事实样本，缺乏理论基础，可能构建出无效的反事实。\n在本研究中，我们力求在实用性和理论保证之间取得平衡，分析一种被称为域反事实的特定类型因果查询，该查询假设样本若在不同域（或环境）中生成会呈现何种形态。我们证明，估计域反事实无需恢复潜在SCM，从而规避了部分理论难题。通过假设干预的可逆性和稀疏性，我们证明域反事实估计误差可由数据拟合项和干预稀疏项界定。基于我们的理论结果，我们开发了一种具备理论依据的实用算法，将建模过程简化为在自回归和共享参数约束下的生成模型估计，以此强制实现干预稀疏性。最后，我们通过大量模拟实验和基于图像的实验表明，该方法在反事实估计上优于基线方法。
英文摘要：
Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the observations are non-linear mixtures of these latent variables, such as pixels in images. One approach is to recover the latent Structural Causal Model (SCM), which may be infeasible in practice due to requiring strong assumptions, e.g., linearity of the causal mechanisms or perfect atomic interventions. Meanwhile, more practical ML-based approaches using naive domain translation models to generate counterfactual samples lack theoretical grounding and may construct invalid counterfactuals. In this work, we strive to strike a balance between practicality and theoretical guarantees by analyzing a specific type of causal query called domain counterfactuals, which hypothesizes what a sample would have looked like if it had been generated in a different domain (or environment). We show that recovering the latent SCM is unnecessary for estimating domain counterfactuals, thereby sidestepping some of the theoretic challenges. By assuming invertibility and sparsity of intervention, we prove domain counterfactual estimation error can be bounded by a data fit term and intervention sparsity term. Building upon our theoretical results, we develop a theoretically grounded practical algorithm that simplifies the modeling process to generative model estimation under autoregressive and shared parameter constraints that enforce intervention sparsity. Finally, we show an improvement in counterfactual estimation over baseline methods through extensive simulated and image-based experiments.
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。
