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Automatic Double Machine Learning for Continuous Treatment Effects

Sylvia Klosin

arXiv 21 Apr 2021 · Econometrics · 2 citations (OpenAlex)

arXiv:2104.10334 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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.

Citation extraction

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and… (2016) Locally robust semiparametric estimation1.00063100%
2Colangelo, K. and Lee, Y.-Y (2020) Double debiased machine learning nonparametric inference with continuous treatments0.9619489%
3Chernozhukov, V., Newey, W. K., and Singh, R (2018) Automatic debiased machine learning of causal and structural effects0.9098475%
4Blundell, R. and Powell, J. L (2001) Endogeneity in nonparametric and semiparametric regression models0.64422100%
5Hansen, B. E (2009) Lecture notes on nonparametrics0.64422100%
6Hernán, M. A. and Robins, J. M (2010) Causal inference0.64422100%
7Kennedy, E. H., Ma, Z., McHugh, M. D., and Small, D. S (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects0.64422100%
8Cantoni, E (2020) A precinct too far: Turnout and voting costs0.5112250%
9Liu, S. and Su, Y (2020) The geography of jobs and the gender wage gap0.5112250%
10Imbens, G (2000) The role of the propensity score in estimating dose-response functions0.51121100%

Showing the top 10 of 29 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application0.51132
2Identification of Treatment Effects under Limited Exogenous Variation0.40511
3Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions0.40511
4An Introduction to Double/Debiased Machine Learning0.40511