Skills

I am As the Co-Director for the RAND Center for Causal Inference, I promote research and development in causal inference through grant-funding, student fellowships, seminars and symposiums. Additionally, I have hands-on experience employing causal inference methods to study complex problems. Within the field of causal inference, my expertise lies in leveraging observational data and/or natural experiments with econometric, machine learning, and other statistical methods to estimate causal effects. The following are examples of methods and associated studies that I have experience employing:

• Randomized control trials (Education study, Economic development study)

• Difference-in-differences (Harm reduction study, Opioid-related mortality study)

• Instrumental variables (Pollution and human capital study)

• Propensity score (Healthcare utilization study)

I have expertise developing and applying methods that combine econometric and machine learning methods. The following are examples of methods and associated studies that I have experience employing:

• Post-double selection LASSO difference-in-differences (Forthcoming, "Using Policy and Innovation to Improve Life-Saving Access to Naloxone")

• Double machine learning (Infant mortality study, Maternal and child health study)

I have experience developing and fielding consumer surveys, multi-level expert surveys, and other population surveys. In these instances, I have worked to develop strategies to maximize response rates, sampling and weighting, longitudinal retention, and other components of surveys. The following are examples of this work:

• Iron supplementation in economic development context

• Quantum computing expert panel and consumer survey

• Healthcare provider survey

• Maternal care processes and outcomes

I have led the economic modeling in many projects, covering cases such as economic input/output, policy simulations, cost analysis, and microsimulations. The following are examples:

• Economic input/output (State film tax incentive study) 

• Policy simulations (Forthcoming "Creaming or Screening: What is the best way to choose among applicants to publicly funded adult education programs?")

• Cost analysis: (Forthcoming "The Infant Health Effects of Doulas: Leveraging Big Data and Machine Learning to Inform Cost-Effective Targeting")

• Microsimulation (Forthcoming "Reducing the Barriers to Methadone: Simulating the Impacts of Methadone Prescribing Policy Changes")

I code every day in my job. While I am most proficient in Stata, I have extensive experience in R as well as Matlab. Furthermore, I am a quick study and have shown the ability to rapidly learn new languages as the context and setting allows

• Mastery: Stata

• Experience: Matlab, R

• Non-recent experience: Python, Julia

While the majority of my research and my training is in quantitative methods, throughout the course of my career I have gained an appreciation for the insight provided by well-designed qualitative studies. I have led a number of projects that incorporate qualitative methods within my research to provide interpretable insights and lived experiences.