*! version 30aug2024

help ddml describe v1.4.4

ddml describe utility for Double Debiased Machine Learning

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 continuous treatment variables and endogeneity, high-dimensional
controls and/or instrumental variables.

ddml describe provides information about the model setup and/or results in detail.

Syntax

ddml describe
[ , mname(name)
sample
learners
crossfit
estimates
all

options

mname(name)
name of the DDML model. Allows to run multiple DDML
models simultaneously. Defaults to m0.

sample
information about the estimation sample, folds, etc.

learners
information about the differ learners used to estimate conditional expectations.

crossfit
information about results of the cross-fitting step.

estimates
information about the estimation estimation results.

all
equivalent to sample + learners + crossfit + estiamtes.

Examples