Learning the Distribution Map in Reverse Causal Performative Prediction

Daniele Bracale; Subha Maity; Moulinath Banerjee; Yuekai Sun

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arXiv · 2405.15172

Learning the Distribution Map in Reverse Causal Performative Prediction

Daniele Bracale·Subha Maity·Moulinath Banerjee·Yuekai Sun

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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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Daniele Bracale, Subha Maity, Moulinath Banerjee, Yuekai Sun. 2024-05-24. Learning the Distribution Map in Reverse Causal Performative Prediction. https://arxiv.org/abs/2405.15172

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