Research
Working Paper and Working in Progress
Individualized Treatment Allocation in Network Game with Complementarity
Individualized Treatment Allocation in Sequential Network Games, with Toru Kitagawa. (2023)
Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes.
Focusing on sequential decision games of interacting agents, this paper develops a method to obtain optimal treatment assignment rules that maximize a social welfare criterion by evaluating stationary distributions of outcomes. Stationary distributions in sequential decision games are given by Gibbs distributions, which are difficult to optimize with respect to a treatment allocation due to analytical and computational complexity. We apply a variational approximation to the stationary distribution and optimize the approximated equilibrium welfare with respect to treatment allocation using a greedy optimization algorithm. We characterize the performance of the variational approximation, deriving a performance guarantee for the greedy optimization algorithm via a welfare regret bound.
Published Paper
Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network, with Toru Kitagawa, (2023).
Journal of Econometrics, 232, 109–131. DOI: 10.1016/j.jeconom.2021.09.009. This paper is linked to the United Nations Sustainable Development Goals.
We develop a procedure to estimate individualized vaccine allocation policy under capacity constraint, exploiting social network data in which we observe individual demographic characteristics and health status.