# Bridging Prediction and Intervention Problems in Social Systems

arXiv: https://arxiv.org/abs/2507.05216 · HTML: https://mlsanity.com/paper/2507.05216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 277 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks](https://mlsanity.com/paper/2106.05498/) (2021)
- [High-Dimensional Prediction for Sequential Decision Making](https://mlsanity.com/paper/2310.17651/) (2023)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024)
- [On the Legal Compatibility of Fairness Definitions](https://mlsanity.com/paper/1912.00761/) (2019)
- [Treatment Effects in Market Equilibrium](https://mlsanity.com/paper/2109.11647/) (2021)
- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [On the Actionability of Outcome Prediction](https://mlsanity.com/paper/2309.04470/) (2023)
- [Conformal Prediction and Human Decision Making](https://mlsanity.com/paper/2503.11709/) (2025)
- [Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment](https://mlsanity.com/paper/2311.13765/) (2023)
- [Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies](https://mlsanity.com/paper/2403.12108/) (2024)
- [Explaining and Improving Information Complementarities in Multi-Agent Decision-making](https://mlsanity.com/paper/2502.06152/) (2025)
- [Modeling Interference Using Experiment Roll-out](https://mlsanity.com/paper/2305.10728/) (2023)
- [Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs](https://mlsanity.com/paper/2208.13323/) (2022)
- [Optimization's Neglected Normative Commitments](https://mlsanity.com/paper/2305.17465/) (2023)
- [Designing Algorithmic Recommendations to Achieve Human-AI Complementarity](https://mlsanity.com/paper/2405.01484/) (2024)
- [Causal Dependence Plots](https://mlsanity.com/paper/2303.04209/) (2023)
- [An Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists](https://mlsanity.com/paper/1912.02943/) (2019)
- [Policy Learning with Competing Agents](https://mlsanity.com/paper/2204.01884/) (2022)
- [An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls](https://mlsanity.com/paper/2111.08664/) (2021)
- [Fair Without Leveling Down: A New Intersectional Fairness Definition](https://mlsanity.com/paper/2305.12495/) (2023)
- [Statistical learning for constrained functional parameters in infinite-dimensional models](https://mlsanity.com/paper/2404.09847/) (2024)
- [Operational Dosage: Implications of Capacity Constraints for the Design and Interpretation of Experiments](https://mlsanity.com/paper/2407.21322/) (2024)
- [Evaluating Prediction-based Interventions with Human Decision Makers In Mind](https://mlsanity.com/paper/2503.05704/) (2025)
- [Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising](https://mlsanity.com/paper/2505.13325/) (2025)
- [Fair Risk Minimization under Causal Path-Specific Effect Constraints](https://mlsanity.com/paper/2408.01630/) (2024)

## Built on by (most cited first, 4 of 4)

- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)
- [LLM Detection as an Intervention: Downstream Impact under Strategic User Behavior](https://mlsanity.com/paper/2607.19300/) (2026)
- [Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness](https://mlsanity.com/paper/2607.21806/) (2026)
- [Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems](https://mlsanity.com/paper/2609.38552/) (2026)

## Abstract (arXiv)

Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders and the limitations of existing organizational, as well as societal, infrastructure and context. In this work, we consider the ways in which we must shift from a prediction-focused paradigm to an intervention-oriented paradigm when considering the impact of ADS within social systems. We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes. We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems, and point to the research directions necessary to operationalize this paradigm shift. Using these tools, we characterize the limitations of focusing on isolated prediction tasks, and lay the foundation for a more intervention-oriented approach to developing and deploying ADS.