The goal of this repo is to run some analysis to support the covariate adjustment blogpost from the Glovo Engineering blog. The blogpost describes some methods to reduce variance using covariates in order to estimate effects in an AB test. In this repo we have code to show, via simulations, the performance of such methods, organized as follows:
- Utilites in
src/to run the desired simulations - Notebooks with the actual simulations in
notebooks/
To install the requirements run:
> poetry installTo run jupyter-lab on this new env, run while in the env:
> python -m ipykernel install --user --name=covariate-adjustment
> jupyter lab
To run the simulations with the comparisons of the covariate adjustment methods
run the notebook on notebooks/covariate_adjustment.ipynb. The other notebook
notebooks/good_bad_covariates.ipynb compare the use of bad and good covariates with
those methods and generate the DAGs of the blogpost.
You can email:
In case you have any doubts or suggestions to improve.