<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bracale, Daniele</style></author><author><style face="normal" font="default" size="100%">Maity, Subha</style></author><author><style face="normal" font="default" size="100%">Banerjee, Moulinath</style></author><author><style face="normal" font="default" size="100%">Sun, Yuekai</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning the Distribution Map in Reverse Causal Performative Prediction</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence and Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Science - Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics - Machine Learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2405.15172</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">arXiv:2405.15172</style></number><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>