###### Abstract  
This study presents an approach to investigating the main interventions related to gains on performance using a combination of educational data mining (EDM) techniques and traditional theory-driven models. The goal is to overcome the limitation of previous EDM studies that lack of causal reasoning, which is a critical concern for educational specialists. We use large-scale assessment data from Brazil and map the main sources of unobserved confounders using causal graphs. We then use a two-way logistic regression fixed effects to account for these confounding factors. The model is evaluated for its predictive ability and further investigated through classification rules and decision trees, resulting in the proposition of new insights into the data. The findings of the study underline the importance of socio-economic factors and showcase the significant impact of faculty education policies as well as the vital role of Brazilian states in these policies.  
educational data mining; educational assessment; causality; ENEM +
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economics. This combination has been recently discussed, but little attention has been paid to the education assessment discipline. By leveraging the strengths of both EDM and traditional models, researchers in this field may be able to produce more accurate and actionable insights into student learning and achievement.  
Additionally, an essential aspect of the literature on causality is the concept of intervention. Interventions can be understood as data variations that change the system [17]. From a temporal perspective, intervention can be modeled in conditional probability, where previous inputs change subsequent outputs [18]. In this paper, we utilize simple economics and machine learning models within a causal framework to identify which interventions have improved educational performance over time. By analyzing interventions in this way, we aim to provide insights into the most impactful strategies for enhancing student learning outcomes.  
It is important to note that while a causal framework can open a path to reduce bias in results, it does not guarantee recovery of the true interventions, as causal inference in observational data is complex and requires many assumptions. Nevertheless, this paper goes beyond most previous EDM studies on LSA data by attempting to find more actionable results that are difficult to see in a purely data-driven approach due to unobserved confounders.  
This work offers a two-fold contribution. Firstly, from a methodological perspective, it introduces a novel interdisciplinary approach to mining interventions associated with educational achievement within a causal framework. This approach combines economics and EDM techniques, presenting a simple and effective solution for presenting new knowledge and informing future policies and practices. Secondly, it provides valuable insights into Brazilian secondary education, utilizing data from the national test (ENEM) and the school census from 2009 to 2019. Brazil is a continent-sized country with a significant economy and the world's second-largest secondary LSA [19], which has received little attention from the scientific community.  
The structure of this paper is as follows. Section 2 describes a brief literature review, while Section 3 introduces an inherent and required background. After that, the overall method is explained in Section 4, and the results are summarized in Section 5. Finally, in Section 6, the conclusion and directions for further work are discussed.