@proceedings {bracale_learning_2024,
	title = {Learning the Distribution Map in Reverse Causal Performative Prediction},
	journal = {International Conference on Artificial Intelligence and Statistics},
	number = {arXiv:2405.15172},
	year = {2025},
	abstract = {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{\textquoteright} 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{\textquoteright} actions. Within this framework, we employ a microfoundation model for the agents{\textquoteright} 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.},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	doi = {10.48550/arXiv.2405.15172},
	url = {http://arxiv.org/abs/2405.15172},
	author = {Bracale, Daniele and Maity, Subha and Banerjee, Moulinath and Sun, Yuekai}
}
