Semi Parametric

Semiparametric statistics combines parametric and nonparametric methods to leverage the strengths of both approaches, aiming for efficient and robust inference in complex settings. Current research focuses on developing semiparametric estimators for causal inference, particularly addressing challenges like hidden variables, high-dimensional data, and model misspecification, often employing machine learning algorithms like neural networks and random forests within a doubly robust framework. These advancements enhance the reliability and applicability of causal inference in diverse fields, including healthcare, economics, and social sciences, by providing more accurate and efficient estimates of treatment effects even with limited data or complex relationships. The development of novel algorithms and theoretical guarantees for semiparametric methods is driving significant progress in statistical modeling and inference.