David Puelz  

Logo

Welcome to my website! I am a principal researcher at the University of Chicago. My research develops computational methods for applied data analysis. I work with Panos Toulis on randomization methods for causal inference. Recent work focuses on experimental data generated from networks.

Here is my CV and Google Scholar.

My identical twin is an Assistant Professor at Baylor College of Medicine. His website can be found here.

Papers


Causal inference and randomizations

A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference, with G Basse, A Feller, and P Toulis (2019).
* R package under development.
* Chicago Booth Review article.

Financial Literacy and Economic Outcomes, with R Puelz (2019).

Regularization and Confounding in Linear Regression for Treatment Effect Estimation, with J He, PR Hahn, and C Carvalho, Bayesian Analysis (2018).

Bayesian methods

A Symmetric Prior for Multinomial Probit Models, with LH Burgette and PR Hahn, Bayesian Analysis (to appear).

Monotonic Effects of Characteristics on Returns, with J Fisher and C Carvalho, Annals of Applied Statistics (to appear).

Portfolio Selection for Individual Passive Investing, with PR Hahn and C Carvalho, Applied Stochastic Models in Business and Industry (2020).

Variable Selection in Seemingly Unrelated Regressions with Random Predictors, with PR Hahn and C Carvalho, Bayesian Analysis (2017).

Regularization in Econometrics and Finance, dissertation (2018).

COVID-19 reviews

Review of: “Firearm Purchasing and Firearm Violence in the First Months of the Coronavirus Pandemic in the United States”, with J Fisher, Rapid Reviews: COVID-19 (to appear).

Talks


Randomization Tests of Causal Effects Under General Interference. Arizona State University (forthcoming) / The University of Chicago Booth School of Business - Econometrics and Statistics Seminar (2019) / Atlantic Causal Inference Conference - McGill University (2019) / International Conference on the Design of Experiments - University of Memphis (2019) / Society for Political Methodology Annual Meeting - MIT (2019) / Design and Analysis of Experiments - UT Knoxville (2019) / Advances with Field Experiments - Chicago Economics (2019).

A Flexible Model for Returns. Seminar on Bayesian Inference in Econometrics and Statistics - Brown University (2019) / Eastern Asia ISBA Conference - Kobe University (Japan, 2019) / The University of Chicago Booth School of Business - Research Workshop (2018).

Posterior Summarization in Finance. International Society for Bayesian Analysis World Meeting - University of Edinburgh (2018).

Regret-based Selection. Seminar on Bayesian Inference in Econometrics and Statistics - Washington University in St. Louis (2017).

Decoupling Shrinkage and Selection. Goldman Sachs. New York, NY (2016).

The ETF Tangency Portfolio. Seminar on Bayesian Inference in Econometrics and Statistics - Washington University in St. Louis (2015).

Betting Against β: A State-space Approach. UT McCombs. Austin, TX (2014).

Dissertation Defense.

Teaching


Machine Learning in Finance. Quantitative Investing Strategies. Spring 2016.

Beauty and Teaching. Pedagogy. Spring 2016.

Mean-variance Portfolios. Quantitative Investing Strategies. Spring 2016.

Betting Against β and The CAPM. Quantitative Investing Strategies. Spring 2015.