Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways

被引:11
作者
Balzer, Laura B. [1 ]
Petersen, Maya L. [2 ]
机构
[1] Univ Massachusetts, Sch Publ Hlth & Hlth Sci, Dept Biostat & Epidemiol, 427 Arnold House, Amherst, MA 01003 USA
[2] Univ Calif Berkeley, Sch Publ Hlth, Div Biostat, Berkeley, CA 94720 USA
关键词
causal inference; causal models; cross-validation; double robustness; machine learning; sample-splitting; Super Learner; MORTALITY; MODELS;
D O I
10.1093/aje/kwab048
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
In this issue of the Journal, Mooney et al. (Am J Epidemiol. 2021;190(8):1476-1482) discuss machine learning as a tool for causal research in the style of Internet headlines. Here we comment by adapting famous literary quotations, including the one in our title (from "Sonnet 43" by Elizabeth Barrett Browning (Sonnets From the Portuguese, Adelaide Hanscom Leeson, 1850)). We emphasize that any use of machine learning to answer causal questions must be founded on a formal framework for both causal and statistical inference. We illustrate the pitfalls that can occur without such a foundation. We conclude with some practical recommendations for integrating machine learning into causal analyses in a principled way and highlight important areas of ongoing work.
引用
收藏
页码:1483 / 1487
页数:5
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