Causal Explanation of the Quality of Parent-Child Interactions with Multimodal Behavioral Features

Abstract

The quality of interactions between parents and children is a critical factor in child development. In recent years, programs have been developed to improve parenting behaviors through evidence-based approaches, such as attachment-based interventions. A vital element of these programs is assessing parenting quality via video recordings of parent-child interactions, which is often labor-intensive and requires specialized expertise. Prior work explored machine learning models to predict expert ratings of parenting behaviors from recordings of semi-structured parent-child play. However, the large set of low-level multimodal features struggled to provide explainable insights, creating barriers to communicating with domain experts and improving the models. In this work, we developed a machine learning pipeline combining sparse multiple canonical correlation analysis with causal discovery and inference techniques to uncover explainable causal relationships between nine categories of behavioral features and expert quality ratings of parent-child interactions. This work provides valuable insights into otherwise black-box models and contributes to the growing body of research on transparent, trustworthy machine learning approaches for modeling parenting behaviors, while offering unique insights into behavioral factors contributing to parenting quality.