Notably, the double machine learning (DML) model proposed by Chernozhukov et al. has garnered widespread attention [2]. Within the framework of a partially linear model, DML allows for the estimation of the average treatment effect. The estimation process can be decomposed into two stages: in ...
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) propose the double machine learning (DML) approach to estimating ATE under a general framework. Compared to the conventional nonparametric regression approach, the DML approach relies on machine learning methods and is suitab...
Notably, the double machine learning (DML) model proposed by Chernozhukov et al. has garnered widespread attention [2]. Within the framework of a partially linear model, DML allows for the estimation of the average treatment effect. The estimation process can be decomposed into two stages: in ...
DoubleML - Double Machine Learning in R The R package DoubleML provides an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018). It is built on top of mlr3 and the mlr3 ecosystem (Lang et al., 2019)....
ddml: Double/debiased machine learning in Stata Mark E Schaffer (Heriot-Watt University, IZA) Achim Ahrens (ETH Zürich) Christian B Hansen (University of Chicago) Thomas Wiemann (University of Chicago) Package website: https://statalasso.github.io/ Latest version available here September 7, ...
We estimate the conditional expectations E [yi |xi , di = 0] and E [yi |xi , di = 1] as well as E [di |xi ] using a supervised machine learner. 9 / 25 DDML models The DDML framework can be applied to other models (all implemented in ddml): Partial linear IV model yi = ...
We use the pre-commit framework to enforce code style and run checks before every commit. To install the pre-commit hooks, make sure you have pre-commit installed (pip install pre-commit) and then run pre-commit install in the root of the repository. This will install the hooks and run...
Adaptive machine learning framework to accelerate ab initio molecular dynamics, Int. J. Quantum Chem. 115, 1074–1083 (2015). 26. Pilania, G., Gubernatis, J. E. & Lookman, T. Structure classification and melting temperature prediction in octet AB solids via machine learning. Phys. Rev. B...
We propose a novel multi-path optimal network service framework to address the problem of cloud service composition. The combination of individual example learning strategy and specular reflection learning strategy introduced into the Golden Eagle optimization algorithm provides new search motivation and dire...
Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptio