Cell-cell communication governs cell fate and decision in health and disease, primarily in the form of biochemical signaling, Understanding what forms of cell-cell communication are present and which can be perturbed is crucial to fully understanding the functionality of biological systems. The recent explosion of single-cell RNA-sequencing has led to the development of cell-cell communication inference methods from gene expression data, enabling new studies on cell-cell communication at unprecedented depth and breadth. These methods reveal possible, simultaneous networks of relationships between cell types that are mediated by cell signaling. In this talk, we present ongoing work that extends cell-cell communication inference output by inferring possible causal relations between signals. That is, does the presence of one signaling interaction cause a subsequent interaction, leading to a flow of information? We show how cell-cell communication and single-cell RNA-sequencing data can be framed in the language of causality and thus draw from existing tools developed for causal discovery. We present some preliminary results of our method that have been applied to synthetic data generated by mathematical modeling and suitable single-cell datasets.