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DDML | Stata ML Page - GitHub Pages ddml provides flexible multi-line syntax and short one-line syntax The multi-line syntax offers a wide range of options, guides the user through the DDML algorithm step-by-step, and includes auxiliary programs for storing, loading and displaying additional information
thomaswiemann ddml: ddml: Double Debiased Machine Learning in R - GitHub ddml is an implementation of double debiased machine learning estimators as proposed by Chernozhukov et al (2018) The key feature of ddml is the straightforward estimation of nuisance parameters using (short-)stacking (Wolpert, 1992), which allows for multiple machine learners to increase robustness to the underlying data generating process
ddml help file | Stata ML Page - GitHub Pages ddml implements algorithms for causal inference aided by supervised machine learning as proposed in Double debiased machine learning for treatment and structural parameters (Econometrics Journal, 2018) Five different models are supported, allowing for binary or continous treatment variables and
Double-debiased machine learning in Stata We introduce ddml for Double-debiased machine learning and pystacked for Stacking (a meta-learning algorithm) Requirement for fast ML implementations: Stata’s Python integration means that we can utilize Python’s ML modules +εi How do we account for confounding factors xi? — The standard approach is to assume linearity g(xi) = x0
Double Debiased Machine Learning in R • ddml - Thomas Wiemann Estimate common causal parameters using double debiased machine learning 'ddml' simplifies estimation based on (short-)stacking, which leverages multiple base learners to increase robustness to the underlying data generating process
ddml: Double debiasedmachinelearningin Stata Chernozhukov et al (2018) propose Double Debiased Machine Learning (DDML) which allow to exploit machine learners other than the Lasso We introduce ddml, which implements DDML for Stata We provide simulation evidence on the finite sample performance of DDML Our recommendation is to use DDML in combination with Stacking Motivating example
CRAN: Package ddml ddml: Double Debiased Machine Learning Estimate common causal parameters using double debiased machine learning as proposed by Chernozhukov et al (2018) <doi:10 1111 ectj 12097>
ddml: Double debiased machine learning in Stata - SAGE Journals In this article, we introduce the package ddml, which implements DDML for Stata 2 ddml adds to a few programs for causal machine learning in Stata (Ahrens, Hansen, and Schafer 2018) We briefly summarize the four main features of the program:
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