I am an expert in a wide range of tools in applied econometrics for predictive and cause-and-effect types of questions using various data types, including spatial panel, longitudinal data, and time series.

I am interested in collecting and constructing my own data, often being the first in specific domains, to best answer the questions at hand.

Although frameworks like propensity score matching, staggered diff-in-diffs, event studies, and synthetic controls are in my arsenal, I am interested in deeply understanding the nature of the problem and discovering natural experiments in observational data to understand causes and effects.

Unlike many applied microeconomists who stop at causal inference, I also interpret cause-and-effect in the context of the main drivers of observations using equilibrium thinking. This often involves building, simulating, and estimating my own models capable of matching empirical causal estimates.