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IVDML: Double Machine Learning with Instrumental Variables and Heterogeneity
Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.
Getting started
Browse package contents
Package details |
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| Author | Cyrill Scheidegger [aut, cre, cph] (<https://orcid.org/0009-0005-2851-1384>) |
| Maintainer | Cyrill Scheidegger <cyrill.scheidegger@stat.math.ethz.ch> |
| License | GPL (>= 3) |
| Version | 1.0.0 |
| URL | https://github.com/cyrillsch/IVDML |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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