Observational Data

Observational data analysis focuses on extracting causal relationships and making predictions from data collected without controlled experiments, addressing limitations of randomized trials. Current research emphasizes developing robust methods to handle confounding variables and missing data, employing techniques like double machine learning, Bayesian inference, and graph neural networks to improve causal effect estimation and policy learning. These advancements are crucial for diverse fields, enabling more reliable insights from readily available observational data in areas such as personalized medicine, e-commerce, and climate science. The development of algorithms that address issues like positivity violations and external validity is a key focus, improving the reliability of inferences drawn from observational studies.