  Affective computing strives to unveil the unknown relationship between affect
elicitation, manifestation of affect and affect annotations. The ground truth
of affect, however, is predominately attributed to the affect labels which
inadvertently include biases inherent to the subjective nature of emotion and
its labeling. The response to such limitations is usually augmenting the
dataset with more annotations per data point; however, this is not possible
when we are interested in self-reports via first-person annotation. Moreover,
outlier detection methods based on inter-annotator agreement only consider the
annotations themselves and ignore the context and the corresponding affect
manifestation. This paper reframes the ways one may obtain a reliable ground
truth of affect by transferring aspects of causation theory to affective
computing. In particular, we assume that the ground truth of affect can be
found in the causal relationships between elicitation, manifestation and
annotation that remain \emph{invariant} across tasks and participants. To test
our assumption we employ causation inspired methods for detecting outliers in
affective corpora and building affect models that are robust across
participants and tasks. We validate our methodology within the domain of
digital games, with experimental results showing that it can successfully
detect outliers and boost the accuracy of affect models. To the best of our
knowledge, this study presents the first attempt to integrate causation tools
in affective computing, making a crucial and decisive step towards general
affect modeling.
