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Using propensity scores in difference-in-differences models to estimate the effects of a policy change

机译：在差异模型中使用倾向得分估算政策变更的影响

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Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the "difference-in-differences" to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the "Alternative Quality Contract" (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees.
机译：差异（DD）方法是一种通用策略，用于评估在特定时间点制定的政策或计划的效果，例如新法律的实施。 DD方法将不受策略干预影响的组的时间变化与受策略干预影响的组的时间变化进行比较，并将“差异”归因于策略的效果。如果在没有干预的情况下，干预组和比较组之间的时间趋势相同，则DD方法可提供无偏效应估计。但是，DD模型的一个问题是，计划和干预小组的方式可能会随时间变化而影响其趋势，或者其组成可能随时间而变化。倾向得分方法在其他非实验研究中通常用于处理这种类型的混淆，但是在DD模型中使用它们时的特殊注意事项尚未得到充分研究。在本文中，我们描述了倾向得分与DD模型结合使用的方法，特别是研究了倾向得分加权策略，该策略对四个组（由时间和干预状态定义）进行加权，以在一组特征上达到平衡。我们讨论了与该方法相关的概念性问题，包括在选择要纳入倾向得分模型的变量时要特别小心，尤其是考虑到分析的多个时间点性质。我们用一个应用程序来说明这种思想和方法，该应用程序估计新的支付和交付系统创新（马萨诸塞州蓝十字蓝盾公司实施的称为“替代质量合同”（AQC）的负责任的护理组织模型）对健康计划参保者自付费用的精神卫生服务支出。我们发现没有证据表明AQC影响了参加者的自费心理健康服务支出。

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