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.

翻译：受州级阿片类药物政策研究的启发，本文提出一种利用自回归模型在面板数据与交错处理采纳场景中进行因果效应估计的新方法。具体而言，我们旨在通过量化强制访问处方药监测计划（PDMPs）、纳洛酮获取法案（NALs）以及医用大麻法案对阿片类药物处方行为的影响，评估关键阿片类相关政策的效果。现有方法（如双重差分法与合成控制法）在这类动态政策环境中应用面临挑战——多重政策随时间逐步实施且样本规模有限。自回归模型作为替代策略虽已在类似场景中用于政策效应评估，但缺乏正式的理论依据。本文提出一组将此类模型与因果效应相关联的假设，并研究了当关键因果假设被违反时基于该方法的估计偏差。通过一系列模拟研究（其数据结构与实际应用场景一致），我们证明所提出的估计量在多数情况下优于现有估计量。简言之，我们为使用自回归模型评估四种州级政策应对阿片类药物危机的有效性提供了理论依据。