Dr. Rivero is an applied econometrician currently investigating novel approaches to causal inference while facing challenging settings of unobserved heterogeneity. Examples include household-level consumption data of addictive goods and historical data on larger units such as nations. More generally, he is interested in panel/longitudinal data analysis on economic agent behavior. He is also interested in the joint measurement of inequality of several economic indicators.As part of his agenda, Dr. Rivero develops techniques towards unsupervised learning and optimal transport theory to estimate causal effects and provide multidimensional rankings/comparisons between joint probability distributions, respectively.