估计员
检查表
公共卫生
计算机科学
口译(哲学)
简单(哲学)
治疗组和对照组
钥匙(锁)
数据科学
计量经济学
心理学
统计
数学
医学
认知心理学
计算机安全
护理部
哲学
认识论
程序设计语言
作者
Coady Wing,Madeline Yozwiak,Alex Hollingsworth,Seth Freedman,Kosali Simon
出处
期刊:Annual Review of Public Health
[Annual Reviews]
日期:2024-01-26
卷期号:45 (1)
被引量:1
标识
DOI:10.1146/annurev-publhealth-061022-050825
摘要
Difference-in-difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DID are an average of simpler 2 × 2 DID subexperiments. Expected final online publication date for the Annual Review of Public Health, Volume 45 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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