mlsanityData to 14 Oct 2026

Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets

arXiv 2404.13240 ↗First posted 20 Apr 2024stat.ML cs.GT cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we compare employers that anticipate the strategic response of a labor force with employers that do not. We show through a combination of theory and experiment that employers with performatively optimal hiring policies improve employer reward, labor force skill level, and in some cases labor force equity. On the other hand, we demonstrate that performative employers harm labor force utility and fail to prevent discrimination in other cases.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

30 of 52 references.

References · 30

  1. 2020Performative Predictioncited by 327
  2. 2017Strategic Classification from Revealed Preferencescited by 128
  3. 2018The Social Cost of Strategic Classificationcited by 105
  4. 2021Outside the Echo Chamber: Optimizing the Performative Riskcited by 96
  5. 2020Stochastic Optimization for Performative Predictioncited by 93
  6. 2019Strategic Classification is Causal Modeling in Disguisecited by 92
  7. 2020Performative Prediction in a Stateful Worldcited by 90
  8. 2021How to Learn when Data Reacts to Your Model: Performative Gradient Descentcited by 71
  9. 2021Strategic Classification in the Darkcited by 66
  10. 2020Causal Strategic Linear Regressioncited by 65
  11. 2020Stochastic optimization with decision-dependent distributionscited by 65
  12. 2021Strategic Classification Made Practicalcited by 59
18 more
  1. 2021Who Leads and Who Follows in Strategic Classification?cited by 53
  2. 2021Information Discrepancy in Strategic Learningcited by 48
  3. 2021Alternative Microfoundations for Strategic Classificationcited by 47
  4. 2019The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationallycited by 41
  5. 2022On-Demand Sampling: Learning Optimally from Multiple Distributionscited by 39
  6. 2022Anticipating Performativity by Predicting from Predictionscited by 37
  7. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  8. 2018Downstream Effects of Affirmative Actioncited by 34
  9. 2022Generalized Strategic Classification and the Case of Aligned Incentivescited by 34
  10. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  11. 2021Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responsescited by 32
  12. 2021How to Learn when Data Gradually Reacts to Your Modelcited by 30
  13. 2021Strategic Rankingcited by 21
  14. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  15. 2021Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributionscited by 17
  16. 2021A linear adjustment based approach to posterior drift in transfer learningcited by 17
  17. 2022Understanding new tasks through the lens of training data via exponential tiltingcited by 11
  18. 2022Predictor-corrector algorithms for stochastic optimization under gradual distribution shiftcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 2.

6 papers.

  1. 2024Learning under Imitative Strategic Behavior with Unforeseeable Outcomescited by 8
  2. 2024Learning the Distribution Map in Reverse Causal Performative Predictioncited by 5
  3. 2024ProFL: Performative Robust Optimal Federated Learningcited by 2
  4. 2024Microfoundation Inference for Strategic Predictioncited by 2
  5. 2026The Role of Causal Features in Strategic Classification for Robustness and Alignmentcited by 1
  6. 2026When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learningcited by 0