Process mining enables organizations to discover, monitor, and improve processes using event data. Traditional process mining methods focus on one object type (a.k.a. case notion) when analyzing a process, such as ‘orders’ in the Order-to-Cash process. This narrow case notion selection can lead to incomplete insights and misleading results. Object-Centric Process Mining (OCPM) addresses this by analyzing the process from multiple object types perspectives, such as ‘orders’ and ‘deliveries.’ However, current OCPM algorithms often create complex models. These models are hard to comprehend for stakeholders as they are either not good at dealing with noise or lack the power to identify important workflow patterns like parallel and exclusive choices and merges. Thus, this paper introduces a new method to discover Object-centric Causal Nets (OCCN). This method builds on Causal nets, which show the causal links between activities. It supports object-centric analysis and handles concurrency and choices better. In this method, we merge redundant process flows using an edge-coarse-graining technique, which makes the models easier to interpret by removing unnecessary visual clutter. We implemented the method in Python. In a user study, we compared OCCN with Object-Centric Petri Nets, and the result shows that OCCN models are easier to understand and help users recognize patterns more effectively.