TAKING ACCOUNT OF TIME LAGS IN CAUSAL-MODELS
TAKING ACCOUNT OF TIME LAGS IN CAUSAL-MODELS
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DOI：
10.1111/j.1467-8624.1987.tb03492.x
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发表时间：
1987-02-01
影响因子：
4.6
通讯作者：
REICHARDT, CS
中科院分区：
文献类型：
--
作者：
GOLLOB, HF;REICHARDT, CS
虽然原因产生效果需要时间，但因果模型往往不能充分考虑时间滞后。特别是，包含横截面关系（即同时两个变量值之间的关系）的因果模型是不令人满意的，因为(a)它们忽略了变量在先前时间的值，(b)它们忽略了变量可能对自身产生的影响，以及(c)它们未能指定正在研究的因果区间的长度。这些遗漏在对因果效应大小的估计中会产生严重的影响。纵向模型也可能不能适当地考虑到时间滞后，这也可能导致严重的估计偏差。讨论说明了横截面模型和纵向模型中可能出现的偏差，介绍了因果建模的潜在纵向方法，并展示了如何使用潜在纵向模型通过考虑时间滞后来减少偏差，即使只有一个时间点的数据可用。
Although it takes time for a cause to exert an effect, causal models often fail to allow adequately for time lags. In particular, causal models that contain cross-sectional relations (i.e., relations between values of 2 variables at the same time) are unsatisfactory because (a) they omit the values of variables at prior times, (b) they omit effects that variables can have on themselves, and (c) they fail to specify the length of the causal interval that is being studied. These omissions can produce severe bioases in estimates of the size of causal effects. Longitudinal models also can fail to take account of time lags properly, and this too can lead to severely biased estimates. The discussion illustrates the biases that can occur in both cross-sectional and longitudinal models, introduces the latent longitudinal approach to causal modeling, and shows how latent longitudinal models can be used to reduce bias by taking account of time lags even when data are available for only 1 point in time.