Graph Neural Networks (GNNs) are a powerful tool for graph representation learning and have been proven to excel in practical complex problems like neural machine translation [1], traffic forecasting [5, 47], or drug discovery [11]. In this work, we investigate to which extent the inductive bias of GNNs-encoding the causal graph information-can be exploited to answer interventional and counterfactual queries. More specifically, to approximate the interventional and counterfactual distributions induced by interventions on a casual model. To this end, we assume i) causal sufficiency-i.e., absence of hidden confounders; and, ii) access to observational data and the true causal graph. We stress that the causal graph can often be inferred from expert knowledge [52] or via one of the approaches for causal discovery [12, 42]. With this analysis we aim to complement the concurrent line of research that theoretically studies the use of Neural Networks (NN) [45], and more recently GNNs [49], for causal inference. To this end, we describe the architectural design conditions that a variational graph autoencoder (VGAE)-as a density estimator that leverages a priori graph structure-must fulfill so that it can approximate causal interventions (do-operator) and abduction-action-prediction steps [33]. The resulting Variational Causal Graph Autoencoder, referred to as VACA, enables approximating the observational, interventional and counterfactual distributions induced by a causal model with unknown structural equations. We remark that parametric assumptions on the structural causal equations are in general not testable, may thus not hold in practice [34] and may lead to inaccurate results, if misspecified.

A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation. An important contextual influence shaping individuals' subjective experience of a video is the personal memories that it triggers in them. Prior research has found that this memory influence explains more variation in video-induced emotions than other contextual variables commonly used for personalizing predictions, such as viewers' demographics or personality. In this article, we show that (1) automatic analysis of text describing their video-triggered memories can account for variation in viewers' emotional responses, and (2) that combining such an analysis with that of a video's audiovisual content enhances the accuracy of automatic predictions. We discuss the relevance of these findings for improving on state of the art approaches to automated affective video analysis in personalized contexts.