Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Keywords: Dose response estimation, Causal Machine Learning, Performance Decomposition, Datasets
TL;DR: We enable the decomposition of performance of dose response estimators on various benchmarking datasets to find the true sources of model perfromance.
Abstract: Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariates, interventions, doses, and outcomes. In recent years, the machine learning community has shown great interest in developing tailored CADR estimators that target specific challenges. Their performance is typically evaluated against other methods on (semi-) synthetic benchmark datasets. Our paper analyses this practice and shows that using popular benchmark datasets without further analysis is insufficient to judge model performance. Established benchmarks entail multiple challenges, whose impacts must be disentangled. Therefore, we propose a novel decomposition scheme that allows the evaluation of the impact of five distinct components contributing to CADR estimator performance. We apply this scheme to eight popular CADR estimators on four widely-used benchmark datasets, running nearly 1,500 individual experiments. Our results reveal that most established benchmarks are challenging for reasons different from their creators' claims. Notably, we find that confounding - the key challenge that motivated recent methods - does not significantly affect CADR estimation performance for the considered datasets. We discuss the major implications of our findings and present directions for future research.
Primary Area: datasets and benchmarks
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Submission Number: 7446
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