Package: ddml 0.2.2.9000

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' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base learners to increase robustness to the underlying data generating process.

Authors:Achim Ahrens [aut], Christian B Hansen [aut], Mark E Schaffer [aut], Thomas Wiemann [aut, cre]

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Peer review:

Bug tracker:https://github.com/thomaswiemann/ddml/issues

On CRAN:

14 exports 15 stars 2.15 score 75 dependencies 25 scripts 280 downloads

Last updated 3 months agofrom:40efcee543. Checks:OK: 7. Indexed: yes.

Exports:crosspredcrossvalddml_ateddml_attddml_fplivddml_lateddml_plivddml_plmmdl_glmmdl_glmnetmdl_rangermdl_xgboostolsshortstacking

Dependencies:abindAERbackportsbootbroomcarcarDataclicodetoolscolorspacecowplotcpp11data.tableDerivdoBydplyrfansifarverforeachFormulagenericsggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnnlsnumDerivpbkrtestpillarpkgconfigpurrrquadprogquantregR6rangerRColorBrewerRcppRcppEigenrlangsandwichscalesshapeSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxgboostzoo