面板数据因果推断的自回归模型及其在州级阿片类药物政策中的应用
Autoregressive models for panel data causal inference with application to state-level opioid policies
AI总结：
本文提出基于自回归模型的因果推断方法，用于评估州级阿片类药物政策（PDMPs、NALs、医用大麻法）的效果，并通过模拟验证其优于现有方法。
摘要：
受州阿片类药物政策研究的启发，我们提出了一种新颖方法，利用自回归模型在具有面板数据和交错处理采纳的设置中进行因果效应估计。具体而言，我们旨在通过量化必须访问处方药监测计划（PDMPs）、纳洛酮获取法（NALs）和医用大麻法对阿片类药物处方的影响，来评估关键阿片类药物相关政策的效应。现有方法，如双重差分和合成控制法，在多种政策随时间实施且样本量较小的动态政策环境中难以应用。自回归模型是在类似设置中用于估计政策效应的替代策略，但直到本文之前缺乏正式论证。我们概述了一组将这些模型与因果效应联系起来的假设，并研究了当关键因果假设被违反时基于此方法的估计偏差。在一系列模拟研究中，我们模拟了应用的结构，表明我们提出的估计器通常优于现有估计器。简而言之，我们论证了使用自回归模型评估四项州政策在对抗阿片类药物危机中的有效性。
英文摘要：
Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption. Specifically, we seek to estimate the impact of key opioid-related policies by quantifying the effects of must access prescription drug monitoring programs (PDMPs), naloxone access laws (NALs), and medical marijuana laws on opioid prescribing. Existing methods, such as differences-in-differences and synthetic controls, are challenging to apply in these types of dynamic policy landscapes where multiple policies are implemented over time and sample sizes are small. Autoregressive models are an alternative strategy that have been used to estimate policy effects in similar settings, but until this paper have lacked formal justification. We outline a set of assumptions that tie these models to causal effects, and we study biases of estimates based on this approach when key causal assumptions are violated. In a set of simulation studies that mirror the structure of our application, we show that our proposed estimators frequently outperform existing estimators. In short, we justify the use of autoregressive models to evaluate the effectiveness of four state policies in combating the opioid crisis.
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