As the primary subtask of sentiment analysis, aspect-based sentiment classification (ABSC) aims to predict the sentiment polarity for a given aspect. While recent deep neural models for ABSC have shown good performance, their robustness is limited due to their reliance on spurious correlations between aspects and sentiment. Specifically, most existing models tend to assign the most frequent sentiment label to a certain aspect in different sentences, or assume different aspects in the same sentence to have the same sentiment polarity, which is vulnerable due to the aspect biases. In this paper, we propose a causal graph to identify and analyze the causal relationships among treatment variables (e.g., aspect, sentence), intermediate variables (e.g., aspect-aware content), and outcome variables (e.g., sentiment polarity) for ABSC. To address the issue of spurious relationships that mislead sentiment polarity prediction, we introduce a novel causal inference framework called CausalABSC. CausalABSC is model agnostic, allowing integration into existing methods. We conduct extensive experiments on five benchmark datasets, which demonstrate the state-of-the-art performance of CausalABSC and its effectiveness in debiasing.