Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
This addresses a longstanding challenge in fields like epidemiology and economics where randomized trials are infeasible, offering incremental improvements in causal effect estimation for treatment-dosage scenarios.
The paper tackles the problem of estimating the conditional average causal effect of treatment-dosage pairs from observational data, extending prior work to provide new generalization bounds for continuous dosages and proposing learning objectives that achieve state-of-the-art performance on benchmark datasets.
We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system. This has been a longstanding challenge for fields of study such as epidemiology or economics that require a treatment-dosage pair to make decisions but may not be able to run randomized trials to precisely quantify their effect and heterogeneity across individuals. In this paper, we extend (Shalit et al, 2017) to give new bounds on the counterfactual generalization error in the context of a continuous dosage parameter which relies on a different approach to defining counterfactuals and assignment bias adjustment. This result then guides the definition of new learning objectives that can be used to train representation learning algorithms for which we show empirically new state-of-the-art performance results across several benchmark datasets for this problem, including in comparison to doubly-robust estimation methods.