Automatic Double Machine Learning for Continuous Treatment Effects
This work addresses a methodological challenge in causal inference for researchers and practitioners, representing an incremental improvement by combining existing DML and ADML tools with a novel debiasing approach.
The paper tackles the problem of estimating continuous treatment effects by introducing a new nonparametric estimator for the average dose-response function, achieving asymptotic normality and performing well in simulations compared to current methods.
In this paper, we introduce and prove asymptotic normality for a new nonparametric estimator of continuous treatment effects. Specifically, we estimate the average dose-response function - the expected value of an outcome of interest at a particular level of the treatment level. We utilize tools from both the double debiased machine learning (DML) and the automatic double machine learning (ADML) literatures to construct our estimator. Our estimator utilizes a novel debiasing method that leads to nice theoretical stability and balancing properties. In simulations our estimator performs well compared to current methods.