Systems and products are changed throughout their lifecycle to adapt to users' needs changes or to technological advances, among other reasons. The redesign process consists in modifying one or several parameters to reach the awaited redesign targets (better performance for instance). However, due to dependencies among parameters, changing one parameter may have unintended impacts on others. The problem we study in the redesign process concerns its underlying process of change propagation through the so called dependency model. The dependencies among parameters are correlation or causal. As a first contribution , the paper argues that the most interesting links to identify, model and work on are causalities. Therefore, the challenge to overcome is to identify the existing causal links among parameters using data exploration or expert knowledge mappings. The second contribution discusses a Causal dependencies identification and modelling approach for Redesign process, CaRe in short, which uses the Bayesian Network theory. CaRe is made to generate a causal Bayesian Network that allows evidential and causal inferences, supporting redesign decision-makings. The steps of CaRe are discussed in detail and future research works are presented at the end of the paper.