Partially Performative Prediction

2026-06-05

AI Summary

This work addresses the challenge of data distribution shifts arising jointly from endogenous factors—induced by model decisions—and exogenous factors—stemming from spontaneous environmental changes—after deployment of predictive models. To tackle this issue, the paper introduces a partially actionable prediction framework that, for the first time, unifies the modeling of both types of distributional shifts and formalizes the notions of online actionable stability and optimality. Extending classical actionable prediction theory, the proposed framework establishes a dynamic online learning paradigm that integrates distribution evolution modeling with optimization analysis. Theoretical results demonstrate that, under reasonable assumptions, online strategies such as repeated retraining can effectively track distributional changes and achieve stable convergence.

Abstract

Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, deploying a model can change the population whose patterns the model aims to predict, inducing a distribution shift that is endogenous to the learning system. This perspective departs from classical treatments of distribution shift, where shifts are typically modeled as exogenous changes in the data-generating process. Yet, in practice, distribution shift is rarely one or the other. Predictive models may influence future data through the decisions they support, while the world itself continues to drift for reasons beyond the learner's control. We study partially performative prediction, a framework that captures both endogenous and exogenous sources of distribution shift. The framework generalizes performative prediction by allowing the data distribution to evolve both in response to the deployed model and according to an external, time-varying process. We extend the central notions of performative stability and performative optimality to this setting by defining their online analogues that track the evolving partially performative environment. We analyze practical learning heuristics, including repeated retraining, and characterize when they successfully adapt to partially performative environments.

Problem

• performative prediction
• distribution shift
• endogenous
• exogenous
• feedback loops

Innovation

• partially performative prediction
• distribution shift
• endogenous feedback
• online learning
• performative stability