The Application of Machine Learning Algorithm and Causal Inference in Analyzing Student Performance

Abstract

The causal inference is the process of determining the causality and correlation between different variables in a system. This paper mainly focuses on the introduction of theories of causal inference analysis and application of causal inference on student performance data. The adjustment formula, backdoor criterion and front-door criterion are three different methods to analyze different causal diagrams which can be used to quantify and analyze the causal effect of students' choices on performance and school support. Taking advantage of logistic regression model, counterfactual inference can utilize students' experience to predict the possibility of result under different choice conditions. The Natural Direct Effect in the path-specific effect is used to study the direct effect of sensitive attribute gender on the acquisition of school support, determining the fairness of decision-making of the school support