Neural Code Models (NCMs) are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilities and limitations of such models is becoming critical. However, the abilities of these models are typically measured using automated metrics that often only reveal a portion of their real-world performance. While, in general, the performance of NCMs appears promising, it is currently unknown how such models arrive at decisions or whether practitioners trust NCMs’ outcomes. In this talk, I will introduce doCode, a post hoc interpretability framework specific to NCMs that can explain model predictions. doCode is based upon causal inference to enable programming language-oriented explanations. While the theoretical underpinnings of doCode are extensible to exploring different model properties, we provide a concrete instantiation that aims to mitigate the impact of spurious correlations by grounding explanations of model behavior in properties of programming languages. doCode can generate causal explanations based on Abstract Syntax Tree information and software engineering-based interventions. To demonstrate the practical benefit of doCode, I will present empirical results of using doCode for detecting confounding bias in NCMs.