*! version 30aug2024

help ddml examples v1.4.4

ddml examples 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.

Below is a list of links to all ddml examples.
All the examples in the help files have clickable links and can be run by the user.

Partially-linear model:

Basic example of the partially-linear model with pystacked

Detailed example of the partially-linear model with pystacked

Detailed general example of the partially-linear model with any learner(s)

Estimating the partially-linear model with multiple treatments

Interactive model (ATE, ATET):

Basic example of the interactive model (ATE, ATET) with pystacked

Detailed example of the interactive model (ATE, ATET) with pystacked

Partially-linear IV model:

Basic example of the partially-linear IV model with pystacked

Basic example of the partially-linear IV model with any learner(s)

Flexible partially-linear IV model:

Basic example of the flexible partially-linear IV model with any learner(s)

Detailed example of the flexible partially-linear IV model with any learner(s)

Ineractive IV model (LATE):

Basic example of the interactive IV model (LATE) with pystacked

Detailed example of the interactive IV model (LATE) with pystacked

Stacking regression with ddml:

Detailed discussion of stacking with ddml plus examples

ddml utilities:

Extracting stored information from ddml associative arrays

Describe the model setup and/or results

Save estimated conditional expectations etc. to a csv file

Overlap plots for interactive models

Cluster cross-fitting with ddml:

Cluster sampling with cross-fit folds