Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add benchmark simulation studies #443

Merged
merged 2 commits into from Dec 14, 2021
Merged

Add benchmark simulation studies #443

merged 2 commits into from Dec 14, 2021

Conversation

t-tte
Copy link
Collaborator

@t-tte t-tte commented Dec 14, 2021

Proposed changes

This PR partly addresses issue #109 by adding a notebook comparing meta-learners across a number of simulation setups/parameters.

Types of changes

What types of changes does your code introduce to CausalML?
Put an x in the boxes that apply

  • Bugfix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • [ x] Documentation Update (if none of the other choices apply)

Checklist

Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your code.

  • [ x] I have read the CONTRIBUTING doc
  • [x ] I have signed the CLA
  • Lint and unit tests pass locally with my changes
  • I have added tests that prove my fix is effective or that my feature works
  • I have added necessary documentation (if appropriate)
  • Any dependent changes have been merged and published in downstream modules

Copy link
Collaborator

@paullo0106 paullo0106 left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The notebook looks good and helpful to me! I'm curious though, is it expected that gradient boosting with R-learners would perform worse across other meta-learners and these 4 data generation setups?

@t-tte
Copy link
Collaborator Author

t-tte commented Dec 14, 2021

That's a great question @paullo0106. I suspect it is possible to improve performance by parameter tuning, but I wanted to keep this exercise computationally manageable. In order to fully replicate the results from the paper, one would need to cycle through the hyperparameters specified therein.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

None yet

2 participants