Le Wang
Le Wang is both an econometrician and an applied microeconomist. His research connects innovative methods for distributional and causal analysis with substantive questions about inequality, intergenerational mobility, education, and public policy. His work has been recognized with awards for research, teaching, service, and interdisciplinary scholarship.
Two themes organize his work. The first is distributional heterogeneity: averages can conceal consequential differences across families, regions, and the income distribution. Whether studying gender earnings gaps, returns to schooling, or the transmission of advantage across generations, he examines patterns that a conditional mean cannot capture. Heterogeneity poses philosophical as well as technical challenges, requiring careful thought about how outcomes are defined, measured, and compared alongside new analytical methods.
The second is causal understanding: identifying how policies, institutions, and circumstances shape those outcomes. Credible answers require confronting selection, measurement error, and the limits of available instruments and identifying assumptions. Together, these themes ask how economic outcomes differ and what produces those differences.
These concerns increasingly inform his recent work in causal machine learning and AI, including ICML and EMNLP contributions on multi-agent language models and multimodal model integration. Through Data Science for the Public Good, he also connects student research with problems faced by public agencies and communities, keeping methods grounded in evidence that informs decisions.
At a glance
Latest work
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Selected work
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Awards, appointments, grants, and where I'll be speaking.
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Research
My research falls in three areas: econometric methods for distributional and causal analysis, applied work on inequality, mobility, education, labor, health, and policy, and research at the boundary of economics and AI. Methods and applications are developed together, so many papers belong to more than one area and appear on each page where they apply. Selected publications are listed below; open an area for its full list, including papers under revision and in progress.
All publications
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Teaching
I am an award-winning teacher with a broad teaching record. Over twenty years I have taught at every level, from Principles of Microeconomics for first-year undergraduates to doctoral courses in causal inference and microeconometrics and a master's course in machine learning for economists, along with applied econometrics, and statistics in between. Beyond the classroom, I have built an experiential learning ecosystem in which coursework, mentorship, applied research, and external partnerships reinforce one another, running the Kohl Centre and the Data Science for the Public Good program, where students take on projects for real partners. Past projects are on both sites. Syllabi are available on request.
Recognition
- Diggs Teaching Scholar Award, Virginia Tech, 2026
- President's Associates Presidential Professorship, University of Oklahoma, 2018
- Outstanding Professor Award, University of New Hampshire, 2009, 2011, and 2013
Student evaluations
Ratings consistently high across four institutions, most recently 5.8 out of 6 in doctoral econometrics at Virginia Tech. Course ratings from 2006 to 2026 with the most recent full evaluations. PDF.
Current courses
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Postdoctoral mentoring
Doctoral advising
I have served on more than thirty dissertation committees across four universities, chairing or co-chairing dissertations at Virginia Tech, Oklahoma, Alabama, and New Hampshire. I currently chair or co-chair five dissertations at Virginia Tech and serve on several more, including in Statistics. Former students hold faculty positions, postdocs at UNC and Pittsburgh, and roles at the Federal Housing Finance Agency, the FDA, the World Bank, and in industry.
Prospective students
I look for students who are passionate about economics, about methodology, and about research with real impact. Tell me the question you care about and what data you think can answer it.
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Program building at Virginia Tech, and Leadershiproles in the professional associations.
Directorships & professional leadership
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Books
Two books are in preparation. Both grow out of courses I have taught for years and the gap I kept seeing between how methods are introduced and how they are used. Stay tuned.
Introduction to Causal Inference
A first course in causal inference that starts from the counterfactual, the other self we never observe, and treats the whole distribution of outcomes as the object of interest rather than the mean.
Data Science for Applied Economics
The data science that applied economists need and are rarely taught: working with real data at scale, machine learning alongside econometrics, and the craft of turning analysis into evidence for decisions.
Stay tuned
Drafts, sample chapters, and course materials will appear here as the books take shape. Write if you would like to be notified.
Funded projects
Current and past grants as PI, Co-PI, and Co-I, from USDA NIFA, HHS, the Institute for Research on Poverty, Virginia Cooperative Extension, and Virginia Tech.
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Editorial
Journal editing and peer review. I'm always glad to see careful distributional and causal work at any of these outlets.
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Referee for
Selected journals; not an exhaustive list.
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Contact
Email is the fastest way to reach me. Prospective doctoral students: say what question you want to work on. Press: please note your deadline in the subject line.