Clustered into control: Heterogeneous causal impacts of water infrastructure failure
利用华盛顿特区水管爆裂数据和每小时交通速度面板，通过k均值聚类识别异质性因果效应，发现水管故障对交通拥堵的影响在不同街道集群间差异显著，但总体福利影响较小。
Abstract We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k ‐means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall.