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Workshop: Causal Machine Learning for Real-World Impact

Counterfactual Decision Support Under Treatment-Conditional Outcome Measurement Error

Luke Guerdan · Amanda Coston · Kenneth Holstein · Steven Wu


Growing work in algorithmic decision support proposes methods for combining predictive models with human judgment to improve decision quality. A challenge that arises in this setting is predicting the risk of a decision-relevant target outcome under multiple candidate actions. While counterfactual prediction techniques have been developed for these tasks, current approaches do not account for measurement error in observed labels. This is a key limitation because in many domains, observed labels (e.g., medical diagnoses, defendant re-arrest) serve as a proxy for the target outcome of interest (e.g., biological medical outcomes, recidivism). We develop a method for counterfactual prediction of target outcomes observed under treatment-conditional outcome measurement error (TC-OME). Our method minimizes risk with respect to target potential outcomes given access to observational data and estimates of measurement error parameters. We also develop a method for estimating error parameters in cases where these are unknown in advance. Through a synthetic evaluation, we show that our approach achieves performance parity with an oracle model when measurement error parameters are known and retains performance given moderate bias in error parameter estimates.

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