New Developments in Methodology for Program Evaluation

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This research project will develop new statistical methods for program evaluation with the goal of improving their reliability and scope in real-life applications. Program evaluation methods are widely used in a variety of disciplines in the social, behavioral, and biomedical sciences. These methods are empirically employed to determine the causal effect of a policy, intervention, or treatment on some outcome of interest. For example, program evaluation methods allow policy makers to improve social programs or public health officials to better target the mitigation of infectious diseases. This project will develop new identification, estimation, and inference results for three modern program evaluation methods: binscatter methods, synthetic control methods, and regression discontinuity methods. The results of this research will provide researchers with new methodological tools to improve empirical work. Graduate students will be mentored and trained in the new methodologies and provided with first-hand research experience. General-purpose software will be developed to implement the new methods.This research project will provide new developments in mathematical statistics and econometrics. For binscatter methods, which are closely related to machine learning techniques, new uniform inference methods will be developed that build on strong approximation tools from probability theory. These results will provide valid confidence bands and hypothesis tests about shape restrictions for causal inference models with possibly heterogenous treatment effects. For synthetic control methods, novel prediction intervals will be developed using non-asymptotic probability concentration ideas from the high-dimensional statistical literature. Practical implementation of these prediction intervals based on the bootstrap also will be studied. For regression discontinuity designs, the project will develop new identification, estimation, inference, and falsification methods for settings with duration-type outcomes and allowing for covariate-adjustment and multiple cutoffs. Robust bias correction inference methods will be developed along with tuning parameter selection methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.