# Classification Under Strategic Self-Selection

arXiv: https://arxiv.org/abs/2402.15274 · HTML: https://mlsanity.com/paper/2402.15274/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [On Calibration and Out-of-domain Generalization](https://mlsanity.com/paper/2102.10395/) (2021)
- [Strategic Classification is Causal Modeling in Disguise](https://mlsanity.com/paper/1910.10362/) (2019)
- [Strategic Classification in the Dark](https://mlsanity.com/paper/2102.11592/) (2021)
- [Stochastic optimization with decision-dependent distributions](https://mlsanity.com/paper/2011.11173/) (2020)
- [PAC-Learning for Strategic Classification](https://mlsanity.com/paper/2012.03310/) (2020)
- [Information Discrepancy in Strategic Learning](https://mlsanity.com/paper/2103.01028/) (2021)
- [Alternative Microfoundations for Strategic Classification](https://mlsanity.com/paper/2106.12705/) (2021)
- [Anticipating Performativity by Predicting from Predictions](https://mlsanity.com/paper/2208.07331/) (2022)
- [Generalized Strategic Classification and the Case of Aligned Incentives](https://mlsanity.com/paper/2202.04357/) (2022)
- [Strategic Classification under Unknown Personalized Manipulation](https://mlsanity.com/paper/2305.16501/) (2023)
- [Plug-in Performative Optimization](https://mlsanity.com/paper/2305.18728/) (2023)
- [Linear Classifiers that Encourage Constructive Adaptation](https://mlsanity.com/paper/2011.00355/) (2020)
- [Fair Sequential Selection Using Supervised Learning Models](https://mlsanity.com/paper/2110.13986/) (2021)
- [Improving Screening Processes via Calibrated Subset Selection](https://mlsanity.com/paper/2202.01147/) (2022)
- [Strategic Classification with Graph Neural Networks](https://mlsanity.com/paper/2205.15765/) (2022)
- [One-Shot Strategic Classification Under Unknown Costs](https://mlsanity.com/paper/2311.02761/) (2023)
- [Modeling Attrition in Recommender Systems with Departing Bandits](https://mlsanity.com/paper/2203.13423/) (2022)
- [Strategic Apple Tasting](https://mlsanity.com/paper/2306.06250/) (2023)
- [Classification with Strategically Withheld Data](https://mlsanity.com/paper/2012.10203/) (2020)
- [Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline](https://mlsanity.com/paper/2203.07513/) (2022)
- [Sequential Strategic Screening](https://mlsanity.com/paper/2301.13397/) (2023)
- [What Makes A Good Fisherman? Linear Regression under Self-Selection Bias](https://mlsanity.com/paper/2205.03246/) (2022)
- [The Gatekeeper Effect: The Implications of Pre-Screening, Self-selection, and Bias for Hiring Processes](https://mlsanity.com/paper/2312.17167/) (2023)
- [On the Within-Group Fairness of Screening Classifiers](https://mlsanity.com/paper/2302.00025/) (2023)
- [Screening with Disadvantaged Agents](https://mlsanity.com/paper/2305.18068/) (2023)

## Built on by (most cited first, 7 of 7)

- [Learning under Imitative Strategic Behavior with Unforeseeable Outcomes](https://mlsanity.com/paper/2405.01797/) (2024)
- [A Market for Accuracy: Classification under Competition](https://mlsanity.com/paper/2502.18052/) (2025)
- [Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality](https://mlsanity.com/paper/2505.05211/) (2025)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Multi-Level Strategic Classification: Incentivizing Improvement through Promotion and Relegation Dynamics](https://mlsanity.com/paper/2602.11439/) (2026)
- [Adjusting Pretrained Backbones for Performativity](https://mlsanity.com/paper/2410.04499/) (2024)
- [Evolutionary Prediction Games](https://mlsanity.com/paper/2503.03401/) (2025)

## Abstract (arXiv)

When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consider user actions that manifest as feature modifications, we study a novel setting in which users decide -- in response to the learned classifier -- whether to at all participate (or not). For learning approaches of increasing strategic awareness, we study the effects of self-selection on learning, and the implications of learning on the composition of the self-selected population. We then propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that both complement our analysis and demonstrate the utility of our approach.