Chris Liao

I am an applied microeconomist interested in studying the digital economy and the economics of technology and organizations. My recent work leverages large-scale microdata on open source software organizations to learn what practices enable organizations to be more resilient to the departures of important members.

Since 2024, I have been a Predoctoral Research Associate at Harvard Business School working for Jesse Shapiro. I graduated from the University of Chicago in 2024, with a bachelor’s in Economics (Honors) and a master’s in Computer Science. In college, I interned at Citadel LLC as a Quantitative Trading Intern and worked as a research assistant for professors at Chicago Booth and NYU Stern.

news

Dec 15, 2025	The working paper for Generalized Random Forest Event Studies is now available on my research page. This brief note shows how generalized random forests can be used to estimate heterogeneous treatment effects in dynamic event study settings.
Dec 13, 2025	The working paper for Organizational Resilience: Evidence from Open Source Software is now available on my research page. This paper studies what organizational practices are associated with increased organizational resilience. This doubled as my writing sample for graduate school applications.

selected research

Organizational Resilience: Evidence from Open Source Software

Christopher Liao

Dec 2025

Abstract Bib PDF

I conduct a large-scale empirical study of open source software organizations to examine which organizational practices affect organizational resilience. Measuring resilience by the impact of a key member’s exit on software development and release activity, I find that on average, organizational outcomes drop permanently by up to 0.8 standard deviations post-departure. I then develop a novel method combining flexible machine learning models with event studies that separates organizations into groups that differ markedly in measured resilience. Leveraging interpretable machine learning methods, I show that practices highlighted in the existing literature do not enhance resilience on their own, but do enhance resilience through complementarities with other practices.
@article{LiaoResilience2025, title = {Organizational Resilience: Evidence from Open Source Software}, author = {Liao, Christopher}, year = {2025}, month = dec, url = {liaochris.github.io/assets/pdf/OrgResilience.pdf}, }
Generalized Random Forest Event Studies

Christopher Liao

Dec 2025

Abstract Bib PDF

This note introduces a procedure for estimating heterogeneous dynamic treatment effects in event studies with staggered adoption, allowing heterogeneity to vary by treatment adoption date and with observation-level time-invariant covariates. The approach reformulates the problem as estimating a conditional linear model with binary regressors and estimates all cohort–event time conditional treatment effects jointly using a single generalized random forest, with propensity scores adapted to staggered treatment adoption in panel settings.
@article{LiaoForest2025, title = {Generalized Random Forest Event Studies}, author = {Liao, Christopher}, year = {2025}, month = dec, url = {liaochris.github.io/assets/pdf/ForestEventStudy.pdf}, }