A Causal Inference Seminar with Stefan Wager.
Read the paper here.
In evaluating social programs, it is important to measure treatment effects within a market economy, where interference arises due to individuals buying and selling various goods at the prevailing market price. We introduce a stochastic model of potential outcomes in market equilibrium, where the market price is an exposure mapping. We prove that average direct and indirect treatment effects converge to interpretable mean-field treatment effects, and provide estimators for these effects through a unit-level randomized experiment augmented with randomization in prices. We also provide a central limit theorem for the estimators that depends on the sensitivity of outcomes to prices. For a variant where treatments are continuous, we show that the sum of direct and indirect effects converges to the total effect of a marginal policy change. We illustrate the coverage and consistency properties of the estimators in simulations of different interventions in a two-sided market.
Stefan is an associate professor of Operations, Information, and Technology at the Stanford Graduate School of Business, and an associate professor of Statistics (by courtesy). Stefan’s research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to problems in statistics, economics and decision making that leverage recent advances in machine learning.