论文标题
结果驱动治疗何时破坏并行趋势？
When Do Outcome Driven Treatments Break Parallel Trends?
论文作者
论文摘要
在什么情况下，如果治疗决策是基于结果的过去值，则对差异（ID）研究所需的平行趋势假设构成威胁？我们通过仿真研究探索平行趋势是否存在于数据生成过程中，通常有利于平行趋势（随机步行，隐藏的马尔可夫模型和持续的直接添加混杂），研究设计（从未得到治疗，尚未治疗，或后来治疗的对照组）以及治疗的结果反应性（是的或没有）。我们将模拟结果的结果解释为当治疗受到过去的结果影响时，平行趋势通常不是一个可信的假设。这是由于对对照组的回归和未来治疗值的选择的结合。由于治疗启动的时间通常受到治疗针对结果的过去结果的影响，因此通常通常更适合研究干预的意外后果？
Under what circumstances is it a threat to the parallel trends assumption required for Difference in Differences (DiD) studies if treatment decisions are based on past values of the outcome? We explore via simulation studies whether parallel trends holds across a grid of data generating processes generally conducive to parallel trends (random walk, Hidden Markov Model, and constant direct additive confounding), study designs (never treated, not yet treated, or later treated control groups), and outcome responsiveness of treatment (yes or no). We interpret the upshot of our simulation results to be that parallel trends is typically not a credible assumption when treatments are influenced by past outcomes. This is due to a combination of regression to the mean and selection on future treatment values, depending on the control group. Since timing of treatment initiation is frequently influenced by past outcomes when the treatment is targeted at the outcome, perhaps DiD is generally better suited for studying unintended consequences of interventions?
