Data Scientist specializing in Causal Inference
Engineer in statistics and data analysis with deep expertise in causal inference, econometrics, and machine learning. I build rigorous statistical methods to answer hard business questions: not just what happened, but why. Specialized in instrumental variables, synthetic controls, and difference-in-differences estimation. Passionate about turning complex data into actionable insights for product, analytics, and research teams.
Estimated the causal effect of ad exposure on user visits using Double ML with an Instrumental Variable (DMLIV) on the Criteo incrementality dataset (25M rows).
Estimated the causal effect of crossing the FICO 675 credit score threshold on approved loan amounts using sharp regression discontinuity on Lending Club data (2.26M loans). Rigorous assumption testing with McCrary density, covariate smoothness, bandwidth sensitivity, and placebo cutoffs.
Estimated heterogeneous treatment effects in staggered adoption settings using the Callaway & Sant'Anna (2020) estimator, addressing TWFE bias with treatment effect heterogeneity.
Estimated the true causal effect of education on earnings using Two-Stage Least Squares with college proximity as an exogenous instrument on Card (1995) data (3,010 men).
Estimated causal treatment effects in observational settings using synthetic control methodology, creating weighted combinations of control units to match the treated unit's pre-treatment trajectory.
Download my full resume to see my professional background and experience
Download Resume