Result: Understanding and Analysing Causal Relations through Modelling using Causal Machine Learning

The study of causal inference has gained significant attention in artificial intelligence (AI) and machine learning (ML), particularly in areas such as explainability, automated diagnostics, reinforcement learning, and transfer learning.. This research applies causal inference techniques to analyze student placement data, aiming to establish cause-and-effect relationships rather than mere correlations. Using the DoWhy Python library, the study follows a structured four-step approach—Modeling, Identification, Estimation, and Refutation—and introduces a novel 3D framework (Data Correlation, Causal Discovery, and Domain Knowledge) to enhance causal modeling reliability. Causal discovery algorithms, including Peter Clark (PC), Greedy Equivalence Search (GES), and Linear Non-Gaussian Acyclic Model (LiNGAM), are applied to construct and validate a robust causal model. Results indicate that internships (0.155) and academic branch selection (0.148) are the most influential factors in student placements, while CGPA (0.042), projects (0.035), and employability skills (0.016) have moderate effects, and extracurricular activities (0.004) and MOOCs courses (0.012) exhibit minimal impact. This research underscores the significance of causal reasoning in higher education analytics and highlights the effectiveness of causal ML techniques in real-world decision-making. Future work may explore larger datasets, integrate additional educational variables, and extend this approach to other academic disciplines for broader applicability.