ABSTRACT

Researchers in Human-Computer Interaction typically rely on experiments to assess the causal effects of experimental conditions on variables of interest. Although this classic approach can be very useful, it offers little help in tackling questions of causality in the kind of data that are increasingly common in HCI – capturing user behavior ‘in the wild.’ To analyze such data, model-based regressions such as cross-lagged panel models or vector autoregressions can be used, but these require parametric assumptions about the structural form of effects among the variables. To overcome some of the limitations associated with experiments and model-based regressions, we adopt and extend ‘empirical dynamic modelling’ methods from ecology that lend themselves to conceptualizing multiple users’ behavior as complex nonlinear dynamical systems. Extending a method known as ‘convergent cross mapping’ or CCM, we show how to make causal inferences that do not rely on experimental manipulations or model-based regressions and, by virtue of being non-parametric, can accommodate data emanating from complex nonlinear dynamical systems. By using this approach for multiple users, which we call ‘multiple convergent cross mapping’ or MCCM, researchers can achieve a better understanding of the interactions between users and technology – by distinguishing causality from correlation – in real-world settings.

摘要

人机交互的研究人员通常依靠实验来评估实验条件对目标变量的因果关系。尽管这种经典方法可能非常有用，但对于解决HCI中越来越常见的那种因果关系问题（在“野外”捕获用户行为），它几乎没有帮助。为了分析此类数据，可以使用基于模型的回归，例如交叉滞后的面板模型或向量自回归，但是这些都需要关于变量之间影响的结构形式的参数假设。为了克服与实验和基于模型的回归相关的某些限制，我们采用并扩展了生态学的“经验动态建模”方法，从而使自己能够将多个用户的行为概念化为复杂的非线性动力学系统。扩展了一种称为“收敛交叉映射”或CCM的方法，我们展示了如何进行不依赖于实验操作或基于模型的回归的因果推理，并且由于其非参数性，因此可以容纳源自复杂非线性动力学的数据系统。通过将这种方法用于多个用户（我们称为“多重收敛交叉映射”或MCCM），研究人员可以在现实环境中通过将因果关系与相关性区分开来更好地理解用户与技术之间的交互。