Victor de Chaisemartin
(né Wang)
Hi there! I'm a third-year PhD student in Information Technology at MIT.
My current research studies how cognitive and decision-making behaviour arises in humans and machines, using tools from AI, behavioural economics and econometrics.
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.
My academic CV is available here (last updated Sep 2026).
CURRENT WORKING PAPERS
Numbers Tell, Words Sell.
With Michael Thaler (UCL) and Mattie Toma (Warwick).
Forthcoming, American Economic Review.
Abstract: When communicating numeric estimates with policymakers, journalists, or the general public, experts must choose between using numbers or natural language. We run two experiments to study whether experts strategically use language to communicate numeric estimates in order to persuade receivers. In Study 1, senders communicate probabilities of abstract events to receivers on Prolific, and in Study 2 academic researchers communicate the effect sizes in research papers to government policymakers. When experts face incentives to directionally persuade instead of incentives to accurately inform receivers, they are 25-29 percentage points more likely to communicate using language rather than numbers. Experts with incentives to persuade are more likely to slant language messages than numeric messages in the direction of their incentives, and this effect is driven by those who prefer to use language. Our findings suggest that experts are strategically leveraging the imprecision of language to excuse themselves for slanting more. Receivers are persuaded by experts with directional incentives, particularly when language is used.
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.
OTHER ARTICLES
Estimating Long-Term Treatment Effects without Long-Term Outcome Data
With David Rhys Bernard and Jojo Lee.
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.
Finnegans Wake as an AI's stream-of-consciousness
Abstract: In this article, I argue that Finnegans Wake can be read as a poetic attempt to imagine the hypothetical stream-of-consciousness of a Large Language Model (LLM), a modern type of AI algorithm underlying popular chatbots like ChatGPT. I concentrate on four particular characteristics of the Wake. Firstly, its extensive use of puns and portmanteaus parallels the neuron polysemanticity observed in LLMs, where neurons can encode multiple concepts at once. Secondly, the Wake draws upon many world languages and literatures, similar to an LLM’s training corpus. Thirdly, the Wake’s characters do not have a fixed personality, but shift between multiple personas, just as LLMs like ChatGPT can adopt different personalities based on the user’s promptings. Finally, I argue that both Finnegans Wake and modern LLMs adopt a deeply cyclical approach to language.