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Automatic Double Machine Learning for Continuous Treatment Effects
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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.
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Cited by 1 Pith paper
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Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments
New doubly robust kernel estimators for the derivative of the dose-response curve achieve nonparametric normality with and without the positivity condition, under an additive confounding model in the latter case.
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