Welcome to my website! I am an Assistant Professor at LSU, with interests in microeconomic and econometric theory. To me these interests are connected by a simple motivating question: what can we learn from observing choices made under uncertainty? Across different settings, I combine fundamental economic structure with observed behavior to identify latent objects such as preferences, beliefs, information, and treatment effects. These settings include: (i) discrimination in decision making, in particular distinguishing between its taste-based and statistical origins; (ii) algorithmic behavior and human adoption, where I study how induced incentives and beliefs affect decision-making performance; and (iii) foundational paradigms for empirical inference, including the use of state-dependent stochastic choice data to model endogenous information acquisition and the application of econometric frameworks for causal inference.

Previously I was a postdoc at Harvard University. Before that I completed my PhD at Northwestern University's Kellogg School of Management. Please don't hesitate to get in touch!

Published/Forthcoming Papers

"The ABC's of Who Benefits from Working with AI: Ability, Beliefs, and Calibration," with Andrew Caplin, David Deming, Shangwen Li, Daniel Martin, Ben Weidmann, and Kadachi Ye. Accepted, Management Science. Previously NBER Working Paper 33021.  [Appendix]. 

"Modeling Machine Learning: A Cognitive Economic Approach," with Andrew Caplin and Daniel Martin. 2025. Journal of Economic Theory 224:105970. 

"Rationalizable Learning," with Andrew Caplin and Daniel Martin. 2024. Economic Theory.

"Parallel Trends and Dynamic Choices," with Elie Tamer and Xun Tang. 2024. Journal of Political Economy Microeconomics 2(1), 129-171.

"Sharp Bounds in the Latent Index Selection Model." 2024. Journal of Econometrics 238(2), 105561.  

"A Robust Test of Prejudice for Discrimination Experiments," with Daniel Martin. 2022. Management Science (Fast Track) 68(6), 4527-4536. 

"An Absolute Test of Racial Prejudice." 2022. Journal of Law, Economics, and Organization 38(1), 42-91.

"Revenue from Matching Platforms,"  with James Schummer. 2021. Theoretical Economics 16(3), 799-824. 

Working Papers

"Testing Capacity-Constrained Learning," with Andrew Caplin, Daniel Martin, Anastasiia Morozova, and Leshan Xu. arXiv:2502.00195.  Revise and resubmit at Experimental Economics.

Abstract: We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). We show that choice data are consistent with capacity-constrained learning if and only if they satisfy a No Improving (Action or Attention) Switches (NIS) condition. Based on existing experiments in which the incentives for being correct are varied, we find strong evidence that participants fail NIS for a wide range of standard perceptual tasks: identifying the proportion of ball colors, recognizing shapes, and counting the number of balls. However, we find that this is not true for all existing perceptual tasks in the literature, which offers insights into settings where we do or do not expect incentives to impact the extent of attention.

"Heterogeneous Treatment Effects via Linear Dynamic Panel Data Models," with Elie Tamer and Xun Tang. arXiv:2410.19060. 

Abstract: We study the identification of heterogeneous, intertemporal treatment effects (TE) when potential outcomes depend on past treatments. First, applying a dynamic panel data model to observed outcomes, we show that an instrumental variable (IV) version of the estimand in Arellano and Bond (1991) recovers a non-convex (negatively weighted) aggregate of TE plus non-vanishing trends. We then provide conditions on sequential exchangeability (SE) of treatment and on TE heterogeneity that reduce such an IV estimand to a convex (positively weighted) aggregate of TE. Second, even when SE is generically violated, such estimands identify causal parameters when potential outcomes are generated by dynamic panel data models with some homogeneity or mild selection assumptions. Finally, we motivate SE and compare it with parallel trends (PT) in various settings with experimental data (when treatments are sequentially randomized) and observational data (when treatments are dynamic, rational choices under learning).

Education

Ph.D. , Managerial Economics and Strategy, Kellogg School of Management, Northwestern University, 2012-2017

B.S., Economics, B.S., Mathematics, summa cum laude, Tulane University, 2011