Model-Robust Counterfactual Prediction Method

05/19/2017

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by   Dave Zachariah, et al.

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We develop a method for assessing counterfactual predictions with multiple groups. It is tuning-free and operational in high-dimensional covariate scenarios, with a runtime that scales linearly in the number of datapoints. The computational efficiency is leveraged to produce valid confidence intervals using the conformal prediction approach. The method is model-robust in that it enables inferences from observational data even when the data model is misspecified. The approach is illustrated using both real and synthetic datasets.