*! version 30aug2024

help ddml interactive v1.4.4

ddml - estimation of the interactive (ATE, ATET) 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 interactive model
used to obtain estimates of the ATE (average treatment effect)
and ATET (average treatment effect on the treated).
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.

Interactive model [interactive]

Y = g(X,D) + U
D = m(X) + V

which (compared to the partially-linear model
relaxes the assumption that X and D are separable.
D is a binary treatment variable.
We estimate, using a supervised machine
learner, the following conditional expectations:

1. E[Y|X,D=0] and E[Y|X,D=1], jointly added using ddml E[Y|X,D]

2. E[D|X], added using ddml E[D|X]

Examples

Below we demonstrate the use of ddml for the interactive 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 interactive model (ATE, ATET) with pystacked

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