Victor Yaneng Wang
Hi there! I'm a first-year PhD student in Information Technology at MIT.
My primary research interests are in econometrics, machine learning and AI. I am also interested in behavioral and experimental economics, and particularly enjoy researching topics with policy-relevant implications.
Previously, I was a Predoctoral Research Fellow in Economics at the Global Priorities Institute, University of Oxford. I also completed an MPhil in Economic Research and a BA in Economics at the University of Cambridge.
If you have any thoughts or questions I'd love to hear from you! My academic email is vyw20 [at] mit [dot] edu.
WORKING PAPERS
Finding Subgroups with Significant Treatment Effects
With Jann Spiess (Stanford) and Vasilis Syrgkanis (Stanford).
Abstract: Researchers often run resource-intensive randomized controlled trials (RCTs) to estimate the causal effects of interventions on outcomes of interest. Yet these outcomes are often noisy, and estimated overall effects can be small or imprecise. Nevertheless, we may still be able to produce reliable evidence of the efficacy of an intervention by finding subgroups with significant effects. In this paper, we propose a machine-learning method that is specifically optimized for finding such subgroups in noisy data. Unlike available methods for personalized treatment assignment, our tool is fundamentally designed to take significance testing into account: it produces a subgroup that is chosen to maximize the probability of obtaining a statistically significant positive treatment effect. We provide a computationally efficient implementation using decision trees and demonstrate its gain over selecting subgroups based on positive (estimated) treatment effects. Compared to standard tree-based regression and classification tools, this approach tends to yield higher power in detecting subgroups affected by the treatment.
Estimating Long-Term Treatment Effects without Long-Term Outcome Data
With David Rhys Bernard (Open Philanthropy) and Jojo Lee (Oxford).
Abstract: The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. Across methods, we find a negative bias in surrogate estimates. For the M-lasso method, in particular, we investigate reasons for this bias and quantify significant precision gains. This provides evidence that the surrogate index method incurs a bias-variance trade-off.
WORK IN PROGRESS
Numbers Tell, Words Sell
With Michael Thaler (UCL) and Mattie Toma (Warwick).
OTHER RESEARCH
Heterogeneous Effects of US Medicaid Expansions on Health Insurance Coverage
MPhil dissertation. Published in Cambridge student journal Finance and Economics Vision.
Abstract: We use cross-sectional data from the 2014 American Community Survey to estimate the heterogeneous treatment effects of expanding Medicaid on health insurance coverage. In doing so, we provide robust evidence which can help policymakers target future Medicaid policies towards particularly responsive individuals. Medicaid expansions were optional by state, allowing us to identify treatment effects by comparing expansion and non-expansion states. We then estimate heterogeneous treatment effects using a non-parametric machine learning algorithm called a causal forest, which offers several advantages over prior methods in the literature. Most notably, it provides a systematic means to discover, from the data, which variables are most relevant for modelling heterogeneity. We find strong evidence of heterogeneity in our estimated treatment effects. Furthermore, we find that individuals with the largest treatment effects were typically aged 25-34; had a high school diploma as their highest level of education; spoke a language other than English at home; and/or were in private for-profit employment.