Skills & Training
I am a quantitative researcher with a broad skillset in quasi-experimental design and applied microecometrics. Below, I detail some of the skills that allow me to successfully pursue causal inference research to address importat policy questions.
Quasi-experimental Design
I am the Co-Director for the Causal Inference, where I oversee research and development in causal inference. Given the data and settings at RAND, this typically involves using quasi-experimental methodology where methods aim to estimate causality based on natural experiments and statistical approaches that leverage different sources of exogeneity. I have done research using many different quasi-experimental methods, such as difference-in-difference, instrumental variables, weighting and matching estimators, and many others. I am well positioned to thus oversee or do analysis related to causal inference and program evaluation.
Experimental Methods
I designed and oversaw a randomized controlled trial of job training in New Orleans, where nearly 600 individuals were randomized into treatment or control. I have additionally led the design and launch of other experiments, including a field experiment of simulated business enterprises in high schools as well as an experiment on LinkedIn platform.
Survey Design, Weighting, and Sampling
While at RAND, I oversaw the weighting and sampling of the American Life Panel, a longitudinal panel of respondents. There, I generated survey weights for nearly 500 surveys and helped with sampling design in many cases. I also led and helped with the design of several survey instruments for different projects.
Modeling
I have led the economic modeling in many projects, covering cases such as dynamic programming, life cycle models, cost analysis, and microsimulations.
Machine Learning
I co-taught a course on machine learning and causal inference. I have also used machine learning in various aspects of projects, including random forests, non-parametric methodology, and neural networks. While I am in many ways still an ML novice, I am an eager learner!
Coding
Mastery: R, Stata, SQL
Experience: Matlab, Python, Scala
Experience that is not recent: Perl, C, Fortran, SAS