Using propensity scores in difference-in-differences models to estimate the effects of a policy change.
Using propensity scores in difference-in-differences models to estimate the effects of a policy change.
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DOI：
10.1007/s10742-014-0123-z
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发表时间：
2014-12-01
影响因子：
1.5
通讯作者：
Barry, Colleen L
中科院分区：
文献类型：
--
作者：
Stuart, Elizabeth A;Huskamp, Haiden A;Duckworth, Kenneth;Simmons, Jeffrey;Song, Zirui;Chernew, Michael;Barry, Colleen L
差中差（DD）方法是评估在特定时间点（如新法律的实施）制定的政策或计划的效果的常用策略。DD方法将未受策略干预影响的组中随时间的变化与受策略干预影响的组中随时间的变化进行比较，并将“差异中的差异”归因于策略的效果。如果在没有干预的情况下，干预组和对照组之间的趋势随着时间的推移是相同的，DD方法提供无偏效应估计。然而，对DD模型的关注是，计划和干预组可能在影响其趋势的方式上有所不同，或者它们的组成可能随着时间的推移而改变。在其他非实验研究中，倾向评分方法通常用于处理这种类型的混淆，但在DD模型中使用它们时的特殊考虑尚未得到很好的研究。在本文中，我们描述了倾向分数与DD模型的结合使用，特别是研究了倾向分数加权策略，该策略对四组（由时间和干预状态定义）进行加权，以平衡一组特征。我们讨论了与此方法相关的概念问题，包括在选择倾向得分模型中包含的变量时需要谨慎，特别是考虑到分析的多时间点性质。我们通过一个应用程序来说明这些想法和方法，该应用程序估计了一种新的支付和交付系统创新（由马萨诸塞州蓝十字蓝盾实施的一种名为“替代质量合同”（AQC）的问责医疗组织模式）对健康计划参保者自付心理健康服务支出的影响。我们没有发现任何证据表明AQC会影响投保人的自付心理健康服务支出。
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.