Validating Causal Inference Methods
因果推断方法的验证：Credence 生成式框架AI 解读 ↓
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因果推断方法的验证：Credence 生成式框架
- 研究问题
- 在反事实无法完整观测、传统模拟过于简化的情况下，如何评估不同因果估计方法对当前观察样本的潜在适用性？
- 方法与思路
- 作者提出深度生成框架 Credence，使合成数据锚定样本经验分布，并由使用者设定随协变量变化的因果效应和混杂偏差。
- 材料陈述的贡献
- 摘要称框架能生成接近观察样本的数据并评估方法相对表现，通过大量模拟及 Lalonde、Project STAR 数据展示。
- 阅读关注点
- 应核对经验分布匹配的诊断、设定因果真值与混杂机制的方式，以及生成模型错误会如何影响方法比较结论。
- 阅读边界
- 这是预印本，不能据此确认同行评审或期刊接收；贴近观测分布不等于恢复真实反事实，摘要未说明比较结果的可迁移条件。
原文摘要
The fundamental challenge of drawing causal inference is that counterfactual outcomes are not fully observed for any unit. Furthermore, in observational studies, treatment assignment is likely to be confounded. Many statistical methods have emerged for causal inference under unconfoundedness conditions given pre-treatment covariates, including propensity score-based methods, prognostic score-based methods, and doubly robust methods. Unfortunately for applied researchers, there is no `one-size-fits-all' causal method that can perform optimally universally. In practice, causal methods are primarily evaluated quantitatively on handcrafted simulated data. Such data-generative procedures can be of limited value because they are typically stylized models of reality. They are simplified for tractability and lack the complexities of real-world data. For applied researchers, it is critical to understand how well a method performs for the data at hand. Our work introduces a deep generative model-based framework, Credence, to validate causal inference methods. The framework's novelty stems from its ability to generate synthetic data anchored at the empirical distribution for the observed sample, and therefore virtually indistinguishable from the latter. The approach allows the user to specify ground truth for the form and magnitude of causal effects and confounding bias as functions of covariates. Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies.