Why Propensity Scores Should Not Be Used for Matching
Why Propensity Scores Should Not Be Used for Matching
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DOI：
10.1017/pan.2019.11
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发表时间：
2019-10-01
影响因子：
5.4
通讯作者：
Nielsen, Richard
中科院分区：
文献类型：
--
作者：
King, Gary;Nielsen, Richard
我们表明，倾向得分匹配（PSM），一个非常流行的方法预处理数据的因果推理，往往完成相反的预期目标，从而增加不平衡，效率低下，模型依赖性和偏见。PSM的弱点在于它试图近似一个完全随机化的实验，而不是像其他匹配方法那样，一个更有效的完全区组随机化实验。因此，PSM对通常很大一部分的不平衡是唯一盲的，而不平衡可以通过用其他匹配方法近似完全阻塞来消除。此外，在数据平衡到足以近似完全随机化，无论是开始或修剪一些意见后，PSM近似随机匹配，我们表明，增加不平衡，甚至相对于原始数据。虽然这些结果表明研究人员用其他可用的匹配方法之一取代PSM，但倾向分数还有其他有效的用途。
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal-thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.