Trained as a statistician, my primary research interests are in the development and use of methodology to better design and analyze the causal effects of public health and educational interventions. In this way I hope to bridge statistical advances and research practice, working with mental health and educational researchers to identify and solve methodological challenges.

I am particularly interested in the trade-offs in different designs for estimating causal effects, especially in terms of improving internal validity of non-experimental studies and external validity of randomized studies. This translates into two primary research areas. First, one of my primary research areas is in the use of propensity score methods for estimating causal effects in non-experimental studies (essentially as a tool to improve internal validity and reduce confounding). My interests in this area include providing advice for researchers in terms of best practice for estimation, diagnostics, and use of propensity score methods. This also includes investigation of how to handle complexities in propensity score methods, including multilevel data settings, covariate measurement error, and complex survey data. My second primary research area is in methods to assess and enhance the external validity (generalizability) of randomized trial results and enable policymakers to determine how applicable the results of a particular randomized study are to their own target population. I also have interests in handling complexities in randomized experiments, in particular missing data and non-compliance.

The applied areas I focus on include autism, the long-term consequences of adolescent substance abuse, education, mental health services and systems, and the effects of health care reform models on mental health service use.