Panel Data

Panel data analysis focuses on estimating causal effects and making predictions from datasets with repeated observations of the same units over time. Current research emphasizes robust methods for handling unobserved heterogeneity and complex treatment effects, employing techniques like double machine learning, synthetic control methods, and various machine learning algorithms (including neural networks and tree-based models) adapted for panel data structures. These advancements improve the accuracy and interpretability of causal inferences, with applications ranging from evaluating policy interventions to personalized predictions in diverse fields like economics, healthcare, and finance.