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Introduction

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/

Setup

To install the requirements run:

> poetry install

To run jupyter-lab on this new env, run while in the env:

> python -m ipykernel install --user --name=covariate-adjustment
> jupyter lab

Notebooks

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.

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