Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference
Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference
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DOI：
10.1093/ije/dyz132
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发表时间：
2020-12-01
影响因子：
7.7
通讯作者：
Rose, Sherri
中科院分区：
文献类型：
--
作者：
Blakely, Tony;Lynch, John;Rose, Sherri
因果推理需要理论和先验知识来进行结构分析，并且通常不被认为是预测建模应用的竞技场。然而，当代的因果推理方法，基于反事实或潜在结果的方法，通常包括最终估计步骤之前的处理步骤。本文的目的是：（一）概述了最近出现的预测支撑步骤在当代因果推理方法作为一个有用的角度对当代因果推理方法，（二）探讨机器学习的作用（作为一种方法，以“最佳预测”）在因果推理。涵盖的因果推断方法包括倾向评分、治疗权重的逆概率（IPTW）、G计算和目标最大似然估计（TMLE）。机器学习已经更多地用于倾向评分和TMLE，并且有可能增加G计算和IPTW估计的使用。
Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.