Computer Science Department, Stevens Institute of Technology, 1 Castle Point on Hudson, Hoboken, NJ, 07030, US.
Department of Psychology, Lehigh University, Bethlehem, PA, US.
Cogn Res Princ Implic. 2020 Feb 13;5(1):6. doi: 10.1186/s41235-020-0206-z.
Causality is inherently linked to decision-making, as causes let us better predict the future and intervene to change it by showing which variables have the capacity to affect others. Recent advances in machine learning have made it possible to learn causal models from observational data. While these models have the potential to aid human decisions, it is not yet known whether the output of these algorithms improves decision-making. That is, causal inference methods have been evaluated on their accuracy at uncovering ground truth, but not the utility of such output for human consumption. Simply presenting more information to people may not have the intended effects, particularly when they must combine this information with their existing knowledge and beliefs. While psychological studies have shown that causal models can be used to choose interventions and predict outcomes, that work has not tested structures of the complexity found in machine learning, or how such information is interpreted in the context of existing knowledge.
Through experiments on Amazon Mechanical Turk, we study how people use causal information to make everyday decisions about diet, health, and personal finance. Our first experiment, using decisions about maintaining bodyweight, shows that causal information can actually lead to worse decisions than no information at all. In Experiment 2, we test decisions about diabetes management, where some participants have personal domain experience and others do not. We find that individuals without such experience are aided by causal information, while individuals with experience do worse. Finally, our last two experiments probe how prior experience interacts with causal information. We find that while causal information reduces confidence in individuals with prior experience, it has the opposite effect on those without experience. In Experiment 4 we show that our results are not due to an inability to use causal models, and that they may be due to familiarity with a domain rather than actual knowledge.
While causal inference can potentially lead to more informed decisions, we find that more work is needed to make causal models useful for the types of decisions found in daily life.
因果关系本质上与决策相关联，因为原因可以让我们更好地预测未来，并通过显示哪些变量有能力影响其他变量来进行干预以改变未来。机器学习的最新进展使得从观察数据中学习因果模型成为可能。虽然这些模型有可能帮助人类做出决策，但目前还不清楚这些算法的输出是否能改善决策。也就是说，因果推断方法已经在其揭示真实情况的准确性方面进行了评估，但没有评估这些输出对人类使用的效用。仅仅向人们提供更多的信息可能不会产生预期的效果，尤其是当他们必须将这些信息与他们现有的知识和信仰结合起来时。虽然心理学研究表明，因果模型可用于选择干预措施和预测结果，但这些研究尚未测试机器学习中发现的复杂结构，也未测试在现有知识背景下如何解释这些信息。
通过在亚马逊 Mechanical Turk 上进行实验，我们研究了人们如何使用因果信息来做出关于饮食、健康和个人理财的日常决策。我们的第一个实验使用了关于维持体重的决策，结果表明因果信息实际上可能导致比没有信息更糟糕的决策。在实验 2 中，我们测试了关于糖尿病管理的决策，其中一些参与者具有个人领域经验，而另一些参与者则没有。我们发现，没有这种经验的个体可以从因果信息中受益，而有经验的个体则表现更差。最后，我们的最后两个实验探究了先前经验与因果信息的相互作用。我们发现，虽然因果信息会降低有经验的个体的信心，但对没有经验的个体则产生相反的效果。在实验 4 中，我们表明我们的结果不是由于无法使用因果模型造成的，而是由于对某个领域的熟悉程度而不是实际知识造成的。
虽然因果推断有可能导致更明智的决策，但我们发现，需要做更多的工作以使因果模型对日常生活中的决策类型有用。
