Estimating causal effects: considering three alternatives to difference-in-differences estimation.
Estimating causal effects: considering three alternatives to difference-in-differences estimation.
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DOI：
10.1007/s10742-016-0146-8
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发表时间：
2016
影响因子：
1.5
通讯作者：
Sekhon JS
中科院分区：
文献类型：
--
作者：
O'Neill S;Kreif N;Grieve R;Sutton M;Sekhon JS
差异差(DID)估计器提供无偏见的治疗效果估计，在没有治疗的情况下，治疗组和对照组的平均结果随着时间的推移将遵循平行的趋势。这种假设在许多情况下都是不可信的。另一种假设是，潜在的结果与治疗状态无关，取决于过去的结果。本文考虑了三种具有这一假设的方法：综合控制法、滞后因变量(LDV)回归方法和与过去结果的匹配。我们的激励性实证研究是对英国的一项医院绩效工资计划的评估，这是最佳实践关税计划。原始DID分析的结论对方法的选择很敏感。我们进行了蒙特卡罗模拟研究，考察了这些方法的性能。当平行趋势假设成立时，确实产生了无偏见的估计，而替代方法在违反时提供了对治疗效果的较少偏见的估计。在这些情况下，LDV方法产生最有效和最少偏见的估计。本文的在线版本(doi：10.1007/s10742-0160146-8)包含补充材料，授权用户可以使用。
Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods’ performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates. The online version of this article (doi:10.1007/s10742-016-0146-8) contains supplementary material, which is available to authorized users.