mlsanityData to 7 Oct 2026

Automatic Double Machine Learning for Continuous Treatment Effects

arXiv 2104.10334 ↗First posted 21 Apr 2021econ.EM math.ST stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 4 of its 32 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

4 of 32 references.

References · 4

  1. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  2. 2018Policy Evaluation and Optimization with Continuous Treatmentscited by 57
  3. 2016Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensionscited by 50
  4. 2017Non-separable Models with High-dimensional Datacited by 3

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Augmented balancing weights as linear regressioncited by 21
  2. 2023Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learningcited by 11
  3. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  4. 2024Double Machine Learning for Adaptive Causal Representation in High-Dimensional Datacited by 0