Learning the Distribution Map in Reverse Causal Performative Prediction

• URL: http://arxiv.org/abs/2405.15172v1
• Date: Fri, 24 May 2024 03:12:13 GMT
• Title: Learning the Distribution Map in Reverse Causal Performative Prediction
• Authors: Daniele Bracale, Subha Maity, Moulinath Banerjee, Yuekai Sun,
• Abstract summary: We introduce a novel approach to learn the distribution shift from data. 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.
• Score: 29.87210806730226
• License: http://creativecommons.org/licenses/by/4.0/
• 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' 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.

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