I am happy to announce a new package that I have written together with Christian Hansen, Mark Schaffer and Thomas Wiemann.

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables.

The main features are:
  1. ddml supports flexible estimators of causal parameters in five econometric models: (1) the Partially Linear Model, (2) the Interactive Model (for binary treatment), (3) the Partially Linear IV Model, (4) the Flexible Partially Linear IV Model, and (5) the Interactive IV Model (for binary treatment and instrument).
  2. ddml supports data-driven combinations of multiple machine learners via stacking by leveraging pystacked, our complementary Stata frontend relying on the Python library scikit-learn. See our separate Working paper.
  3. Aside from pystacked, ddml can be used in combination with many other existing supervised machine learning programs available in or via Stata. ddml has been tested with lassopack, rforest, svmachines, and parsnip. Indeed, the requirements for compatibility with ddml are minimal: Any eclass-program with the Stata-typical "reg y x'' syntax, support for if conditions and post-estimation predict is compatible with ddml.
  4. ddml provides flexible multi-line syntax and short one-line syntax. The multi-line syntax offers a wide range of options, guides the user through the DDML algorithm step-by-step, and includes auxiliary programs for storing, loading and displaying additional information. We also provide a complementary one-line version called qddml (`quick' ddml), which uses a similar syntax as pdslasso and ivlasso.
You can find more information---including many examples---on our Website or in our Working paper.

You can install ddml from our Github or SSC.

Code:
ssc install ddml
net install ddml, from(https://raw.githubusercontent.com/aahrens1/ddml/master)
Many thanks to Kit Baum for uploading the package to SSC.