# Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift

arXiv: https://arxiv.org/abs/2403.00886 · HTML: https://mlsanity.com/paper/2403.00886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [Foundations of Structural Causal Models with Cycles and Latent Variables](https://mlsanity.com/paper/1611.06221/) (2016)
- [Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions](https://mlsanity.com/paper/1707.06422/) (2017)
- [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://mlsanity.com/paper/1612.01205/) (2016)
- [Outside the Echo Chamber: Optimizing the Performative Risk](https://mlsanity.com/paper/2102.08570/) (2021)
- [Stochastic Optimization for Performative Prediction](https://mlsanity.com/paper/2006.06887/) (2020)
- [Anticipating Performativity by Predicting from Predictions](https://mlsanity.com/paper/2208.07331/) (2022)
- [Making Decisions under Outcome Performativity](https://mlsanity.com/paper/2210.01745/) (2022)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Counterfactual Predictions under Runtime Confounding](https://mlsanity.com/paper/2006.16916/) (2020)
- [Model Transferability With Responsive Decision Subjects](https://mlsanity.com/paper/2107.05911/) (2021)
- [Optimal regimes for algorithm-assisted human decision-making](https://mlsanity.com/paper/2203.03020/) (2022)
- [Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction](https://mlsanity.com/paper/2206.13183/) (2022)
- [Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction](https://mlsanity.com/paper/2302.06804/) (2023)
- [Correcting for Selection Bias and Missing Response in Regression using Privileged Information](https://mlsanity.com/paper/2303.16800/) (2023)

## Built on by (most cited first, 3 of 3)

- [Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions](https://mlsanity.com/paper/2510.21335/) (2025)
- [When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning](https://mlsanity.com/paper/2601.04447/) (2026)
- [2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support](https://mlsanity.com/paper/2602.21889/) (2026)

## Abstract (arXiv)

When predicting a target variable Y from features X, the prediction hatY can be performative: an agent might act on this prediction, affecting the value of Y that we eventually observe. Performative predictions are deliberately prevalent in algorithmic decision support, where a Decision Support System (DSS) provides a prediction for an agent to affect the value of the target variable. When deploying a DSS in high-stakes settings (e.g. healthcare, law, predictive policing, or child welfare screening) it is imperative to carefully assess the performative effects of the DSS. In the case that the DSS serves as an alarm for a predicted negative outcome, naive retraining of the prediction model is bound to result in a model that underestimates the risk, due to effective workings of the previous model. In this work, we propose to model the deployment of a DSS as causal domain shift and provide novel cross-domain identification results for the conditional expectation E[Y | X], allowing for pre- and post-hoc assessment of the deployment of the DSS, and for retraining of a model that assesses the risk under a baseline policy where the DSS is not deployed. Using a running example, we empirically show that a repeated regression procedure provides a practical framework for estimating these quantities, even when the data is affected by sample selection bias and selective labelling, offering for a practical, unified solution for multiple forms of target variable bias.