Department of Psychology, University of Pittsburgh.
J Exp Psychol Gen. 2018 Apr;147(4):485-513. doi: 10.1037/xge0000423.
One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record
从时间序列数据中推断因果关系强度时面临的一个挑战是，原因和/或结果可能会呈现出时间趋势。如果不考虑时间趋势，学习者可能会推断出存在因果关系，而实际上并不存在，甚至可能推断出存在正因果关系，而实际上关系是负的，反之亦然。我们提出，学习者使用一种简单的启发式方法来控制时间趋势——他们不是关注给定时刻的原因和结果的状态，而是关注原因和结果如何从一次观察到下一次观察的变化，我们称之为转换。进行了六项实验来了解人们如何从时间序列数据中推断因果强度。我们发现，参与者确实除了状态之外还使用了转换，这有助于他们做出更准确的因果判断（实验 1A 和 1B）。与数字格式相比，参与者在以自然主义视觉格式呈现刺激时更倾向于使用转换（实验 2），并且转换的效果不是由首因或近因效应驱动的（实验 3）。最后，我们发现参与者主要使用变量变化的方向而不是变化的幅度来估计因果强度（实验 4 和 5）。总之，这些研究为人们经常使用一种简单而有效的启发式方法从时间序列数据中推断因果强度提供了证据。
