Daniel's research focuses on developing and implementing statistical methods in widespread domains, with emphasis on methods for causal inference and survival analysis in biomedical settings from real-life and observational data. Additionally, he maintains ongoing collaborations with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations with their goals often being reaching conclusions about causal effects from rich datasets.