Decisions, such as choosing which treatment to give or which policy to implement, have important consequences, and so, in seeking to make the best decision, one must understand the process of identifying causality. This project aims at developing and applying statistical methods that deliver robust data analysis through the interaction of causal inference, machine learning and optimization for optimally estimating causal effects from observational and experimental studies.
Selected manuscripts within this project:
- Optimal Estimation of Generalized Average Treatment Effects via Kernel Optimal Matching
- More robust estimation of sample average treatment effects using Kernel Optimal Matching in an observational study of spine surgical interventions
- Optimal balancing of time-dependent confounders for marginal structural models