The ability to make causal inferences is important for addressing a wide range of research questions in the social, statistical, economic, and medical sciences. From a societal perspective, it is difficult to determine if a policy intervention is effective in the absence of causal inference. This research project will develop new methods for causal inference. The resulting methods will improve statistical analyses for causal inference, thereby enabling more accurate conclusions from experimental and observational intervention studies across many disciplines. The open source R/Matlab software developed from the research will provide valuable data analysis and educational tools for the scientific community.
Motivated from important real applications like racial disparity in health care service, this research will develop new, general, and accessible weighting methods for drawing causal and covariates-balanced descriptive comparisons from observational data. Population-based observational studies have been increasingly used for drawing causal conclusions, but the complex (and usually unknown) underlying data-generating system poses great challenges to valid causal inference. Weighting methods that originated in survey research are flexible and powerful tools for causal inference, as are covariates-balanced descriptive comparative analysis, but these are arguably less developed compared to regression and matching methods. Research foci will include a unified framework for weighting-based causal inference that extends beyond the traditional inverse probability weights, and propensity score weighting methods for complex survey data with multilevel structure. This research will blend theory and methodological developments with motivating applications in areas including health policy, epidemiology, social sciences, and economics.
