Paul Delatte

Paul Delatte

PhD Candidate at USC

About me

I am a PhD candidate in Economics at the University of Southern California.

I specialize in econometric theory. My research projects tackle statistical and optimization problems commonly encountered in causal inference and forecasting.

I have experience using advanced machine learning methods on large datasets and solving intricate optimization problems. I have helped research teams in industry and academia solve pricing problems, experimental design problems, and portfolio optimization problems.

My CV is available here.

Research interests

  • Statistical decision theory and robust optimization: minimax theorems; numerical approximation of minimax rules and maximin priors; constrained minimax problems and optimization-aware solutions.
  • Semi-parametric inference: bias correction and double machine learning; nearest neighbor methods in semi-parametric problems; bootstrap and resampling in semi-parametric problems.
  • Causal inference and experimental design: model-free prediction-based causal inference; comparing model-based and design-based causal inference; inference with adaptive experiments; A/B tests in duopoly.
  • Predictions, forecasts, and decision-making: ensemble methods; model-free predictive intervals; De Finetti’s theorem and extensions; decision-making with predictions.
  • Philosophy of statistics and probability: limits of model-free inference and prediction; exchangeability, resampling, randomization, and quasi-randomization; inverse problems and unobservables in statistics; limits of causal inference.

Education