Anticipating Performativity by Predicting from Predictions
通过预测值进行预测：预判预测的施为效应AI 解读 ↓
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通过预测值进行预测：预判预测的施为效应
- 研究问题
- 关于人的预测可能改变其目标结果，而预测值通常由协变量确定；如何从观测数据中区分预测本身与协变量的因果影响？
- 方法与思路
- 作者分析因果可识别性，提出预测随机化、已部署模型过参数化和离散预测输出三类情形，再将预测值作为监督学习的输入。
- 材料陈述的贡献
- 摘要称，在相应识别条件成立时，可学习可迁移的函数关系并用于分析新部署模型，同时强调收集数据时记录预测值的重要性。
- 阅读关注点
- 应核对三类情形各自的完整识别条件、预测值实际影响结果的路径，以及跨模型迁移所需的稳定性和实验验证范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要只概述三类可识别情形，不能认为加入预测特征就普遍解决因果识别。
原文摘要
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict. Understanding the causal effect of these predictions on the eventual outcomes is crucial for foreseeing the implications of future predictive models and selecting which models to deploy. However, this causal estimation task poses unique challenges: model predictions are usually deterministic functions of input features and highly correlated with outcomes. This can make the causal effects of predictions on outcomes impossible to disentangle from the direct effect of the covariates. We study this problem through the lens of causal identifiability, and despite the hardness of this problem in full generality, we highlight three natural scenarios where the causal relationship between covariates, predictions and outcomes can be identified from observational data: randomization in predictions, overparameterization of the predictive model deployed during data collection, and discrete prediction outputs. Empirically we show that given our identifiability conditions hold, standard variants of supervised learning that predict from predictions by treating the prediction as an input feature can indeed find transferable functional relationships that allow for conclusions about newly deployed predictive models. These positive results fundamentally rely on model predictions being recorded during data collection, bringing forward the importance of rethinking standard data collection practices to enable progress towards a better understanding of social outcomes and performative feedback loops.