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Found 2 papers, 1 papers with code
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
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.
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.
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