Treatment Effect Estimation for Exponential Family Outcomes using Neural Networks with Targeted Regularization
This work addresses a gap in causal inference for real-world data where outcomes follow non-Gaussian distributions, though it is incremental as it builds on existing targeted regularization methods.
The authors tackled the problem of treatment effect estimation for non-Gaussian outcomes by extending targeted regularization to exponential family distributions, achieving a doubly robust estimator with theoretical convergence guarantees.
Neural Networks (NNs) have became a natural choice for treatment effect estimation due to their strong approximation capabilities. Nevertheless, how to design NN-based estimators with desirable properties, such as low bias and doubly robustness, still remains a significant challenge. A common approach to address this is targeted regularization, which modifies the objective function of NNs. However, existing works on targeted regularization are limited to Gaussian-distributed outcomes, significantly restricting their applicability in real-world scenarios. In this work, we aim to bridge this blank by extending this framework to the boarder exponential family outcomes. Specifically, we first derive the von-Mises expansion of the Average Dose function of Canonical Functions (ADCF), which inspires us how to construct a doubly robust estimator with good properties. Based on this, we develop a NN-based estimator for ADCF by generalizing functional targeted regularization to exponential families, and provide the corresponding theoretical convergence rate. Extensive experimental results demonstrate the effectiveness of our proposed model.