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Real world observational data, together with causal inference, allow theestimation of causal effects when randomized controlled trials are notavailable. To be accepted into practice, such predictive models must bevalidated for the dataset at hand, and thus require a comprehensive evaluationtoolkit, as introduced here. Since effect estimation cannot be evaluateddirectly, we turn to evaluating the various observable properties of causalinference, namely the observed outcome and treatment assignment. We developed atoolkit that expands established machine learning evaluation methods and addsseveral causal-specific ones. Evaluations can be applied in cross-validation,in a train-test scheme, or on the training data. Multiple causal inferencemethods are implemented within the toolkit in a way that allows modular use ofthe underlying machine learning models. Thus, the toolkit is agnostic to themachine learning model that is used. We showcase our approach using arheumatoid arthritis cohort (consisting of about 120K patients) extracted fromthe IBM MarketScan(R) Research Database. We introduce an iterative pipeline ofdata definition, model definition, and model evaluation. Using this pipeline,we demonstrate how each of the evaluation components helps drive modelselection and refinement of data extraction criteria in a way that providesmore reproducible results and ensures that the causal question is answerablewith available data. Furthermore, we show how the evaluation toolkit can beused to ensure that performance is maintained when applied to subsets of thedata, thus allowing exploration of questions that move towards personalizedmedicine.

An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference