Deep neural networks (DNNs) have become the state of the art for object detection tasks in autonomous driving systems (ADS). These models often perform safety-critical tasks, such as pedestrian detection and collision avoidance. Therefore, these models must demonstrate an elevated level of dependability within the operational design domain (ODD). Safety analysis requires a causal perspective to understand the effects of perception influencing factors within an ODD. However, the ODD contains complex causal relations that introduce several sources of confounding bias. This makes it difficult to estimate the causal effect of influencing factors within the ODD using associational metrics. Our framework eliminates confounding bias by taking a counterfactual data-augmentation (CDA) approach to estimate the causal effect of perception influencing factors. Our running example of an influencing factor is “half-occlusions” (visibility range of 40%–60%). Our framework describes a process of identifying relevant half-occlusion characteristics and assigning appropriate augmentations. Finally, a comparative analysis is presented between our causal metric and the associational metric, which is based on conditional probability.