*! version 30aug2024

help ddml partial v1.4.4

ddml - estimation of the partially-linear model in 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 supports a variety of different ML programs, including
but not limited to pystacked and lassopack.
pystacked is the recommended way to specify multiple learners in ddml,
and ddml has integrated support for various features provided by pystacked.

The ddml package also includes the wrapper program qddml,
which uses a simplified one-line syntax, but offers less flexibility.

This help file illustrates usage of the partially-linear model.
For examples of other models,
follow the links in the main ddml help file.

We use Y to denote the outcome variable,
X to denote confounders, and
D to denote the treatment variable(s) of interest.

Partially-linear model [partial]

Y = a.D + g(X) + U
D = m(X) + V

where the aim is to estimate a while controlling for X. To this end,
we estimate the conditional expectations
E[Y|X] and E[D|X] using a supervised machine learner.

Examples

Below we demonstrate the use of ddml for the partially-linear model.
Note that estimation models are chosen for demonstration purposes only and
may be kept simple to allow you to run the code quickly.

1. Basic example of the partially-linear model with pystacked

2. Detailed example of the partially-linear model with pystacked

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

4. Estimating the partially-linear model with multiple treatments