Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of the observed input information. Therefore, this leads to poor predictions of students' knowledge, even if the student performed well in the past knowledge state. In this article, we first employ causal inference for explanatory analysis of KT, then propose a learning algorithm for stable KT based on the analysis outcomes. The proposed approach aims to achieve stable KT by constructing global balanced weights that facilitate estimating feature influence and assessing causal relationships between individual variables and outcome variables. We have proved the approach has effective in accuracy and interpretability through extensive experimentation on real-world datasets. In conclusion, this article has methodological implications for the stable assessment of students' knowledge and provides a reference for personalization and use of intelligence in the educational teaching process.