Gun violence is a major problem in contemporary American society, with tens of thousands injured each year. However, relatively little is known about the effects on family members and how 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 are modified 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：枪支暴力是当代美国社会的一个重大问题，每年有数万人因此受伤。然而，关于其对家庭成员的影响以及不同亚群间效应差异的了解相对有限。为研究这些问题，更广泛而言为弥补因果推断文献中的空白，我们提出一个框架用于分析双重差分设计中的效应修饰或异质性处理效应。我们实施了一种结合轮廓匹配与风险集匹配的新匹配技术，以（i）保持协变量、暴露因素与结局的时间对齐，避免其他常见双重差分方法中的陷阱，以及（ii）明确控制因观测协变量在数据发现亚组间不平衡导致的偏倚。我们的案例研究表明，非致命枪伤对伤者的若干健康结局及其家庭成员的心理健康存在显著且持续的影响。敏感性分析显示，这些结果对未测量混杂偏倚具有中等稳健性。最后，伤者的效应主要受伤害严重程度及其记录意图的修饰，而对家庭而言，当亲属的伤害记录源于袭击、自伤或执法人员干预时，其效应最为强烈。