In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a screening systems. Such shifts in distribution are particularly prevalent in the realm of social computing, yet, the strategies to learn these shifts from data remain remarkably limited. Inspired by a microeconomic model that adeptly characterizes agents' behavior within labor markets, we introduce a novel approach to learn the distribution shift. Our method is predicated on a reverse causal model, wherein the predictive model instigates a distribution shift exclusively through a finite set of agents' actions. Within this framework, we employ a microfoundation model for the agents' actions and develop a statistically justified methodology to learn the distribution shift map, which we demonstrate to be effective in minimizing the performative prediction risk.
View on arXiv@article{bracale2025_2405.15172,
title={ Learning the Distribution Map in Reverse Causal Performative Prediction },
author={ Daniele Bracale and Subha Maity and Moulinath Banerjee and Yuekai Sun },
journal={arXiv preprint arXiv:2405.15172},
year={ 2025 }
}