Comment: The Challenges of Multiple Causes

• 成果类型: Editorial Material
• 署名作者: Imai, Kosuke; Jiang, Zhichao
• 署名单位: Harvard University; Harvard University; University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1689137
• 发表日期: 2019
• 页码: 1605-1610
• 关键词: models

摘要

We begin by congratulating Yixin Wang and David Blei for their thought-provoking article that opens up a new research frontier in the field of causal inference. The authors directly tackle the challenging question of how to infer causal effects of many treatments in the presence of unmeasured confounding. We expect their article to have a major impact by further advancing our understanding of this important methodological problem. This commentary has two goals. We first critically review the deconfounder method and point out its advantages and limitations. We then briefly consider three possible ways to address some of the limitations of the deconfounder method.