指数族合成控制

中文摘要 AI 辅助

我们开发了指数族合成控制（EFSC），这是一种针对数据集面板的合成控制的分布版本。面板中的每个单元格对应于从指数族中抽取的一个数据集，其自然参数在概率上按单位和时间进行因子分解。我们使用黑盒变分推断来估计潜在因子。这将合成控制的通常加权平均视图替换为一个灵活的、作用于完整分布的概率模型。我们提出了基于自然参数后验所诱导的干预前后分布之间散度的因果估计量，并辅以分布安慰剂检验，以支持因果推断并评估估计效应的显著性。我们在合成数据和真实数据上验证了所提出的框架。在各种指数族分布中，EFSC能够准确恢复由指数倾斜引起的因果效应，以及处理分布与反事实分布之间的相应散度。该框架还能捕捉由潜在因子的结构扰动和重尾噪声污染引起的效应。最后，我们将EFSC应用于研究《平价医疗法案》（ACA）下医疗补助的扩展及其对美国各州健康保险覆盖分布的影响。代码可在以下网址获取：此HTTPS URL。

英文摘要

We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the panel corresponds to a dataset drawn from an exponential family whose natural parameters factorize probabilistically across units and times. We estimate the latent factors using black-box variational inference. This replaces the usual weighted-average view of synthetic controls with a flexible probabilistic model that operates on full distributions. We propose causal estimands based on divergences between pre- and post-intervention distributions induced by the posterior of the natural parameters, together with distributional placebo tests to support causal inference and assess the significance of the estimated effects. We validate the proposed framework on synthetic and real data. Across a variety of exponential-family distributions, EFSC accurately recovers causal effects induced by exponential tilts, together with the corresponding divergences between treated and counterfactual distributions. The framework also captures effects induced by structural perturbations of the latent factors and by heavy-tailed noise contamination. Finally, we apply EFSC to study the expansion of Medicaid under the Affordable Care Act (ACA) and its impact on the distribution of health insurance coverage across U.S. states. Code is available at https://github.com/blei-lab/efsc.

发表机构

• Data Science Institute（数据科学研究所）
• Columbia University（哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。