Gun violence is a major source of injury and death in the United States. However, relatively little is known about the effects of firearm injuries on survivors and their family members and how these effects vary across subpopulations. To study these questions and, more generally, to address a gap in the causal inference literature, we present a framework for the study of effect modification or heterogeneous treatment effects in difference-in-differences designs. We implement a new matching technique, which combines profile matching and risk set matching, to (i) preserve the time alignment of covariates, exposure, and outcomes, avoiding pitfalls of other common approaches for difference-in-differences, and (ii) explicitly control biases due to imbalances in observed covariates in subgroups discovered from the data. Our case study shows significant and persistent effects of nonfatal firearm injuries on several health outcomes for those injured and on the mental health of their family members. Sensitivity analyses reveal that these results are moderately robust to unmeasured confounding bias. Finally, while the effects for those injured vary largely by the severity of the injury and its documented intent, for families, effects are strongest for those whose relative's injury is documented as resulting from an assault, self-harm, or law enforcement intervention.

翻译：枪击暴力是美国伤害和死亡的主要来源。然而，关于枪伤对幸存者及其家庭成员的影响以及这些影响在不同亚群体中的差异，目前所知相对较少。为研究这些问题，并更广泛地填补因果推断文献中的空白，我们提出了一个用于研究双重差分设计中效应修饰或异质性处理效应的框架。我们实施了一种新的匹配技术，结合了轮廓匹配和风险集匹配，以（i）保持协变量、暴露和结果的时间对齐，避免其他常见双重差分方法的缺陷，以及（ii）明确控制由于从数据中发现的亚组内观测协变量不平衡导致的偏倚。我们的案例研究表明，非致命枪伤对受伤者的多项健康结果及其家庭成员的心理健康具有显著且持续的影响。敏感性分析显示，这些结果对未测量的混杂偏倚具有中等程度的稳健性。最后，虽然对受伤者的影响在很大程度上因伤害的严重程度及其记录意图而异，但对于家庭成员而言，影响在亲属伤害被记录为源于袭击、自残或执法干预的情况下最为强烈。