-
Notifications
You must be signed in to change notification settings - Fork 776
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
Causal trees option to return counterfactual outcomes #623
Conversation
Fix CI tests job & dependencies
I updated package dependencies to make tests pass as expected and suggest moving requirements and metadata from setup.py to As a bonus, now you don't need to execute |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks a lot! This will make installation and package management much easier. Really appreciate your contribution - as always!
Proposed changes
Hi,
This feature adds an option for causal trees to return counterfactual outcomes$\hat{Y}(X|T=0)$ , $\hat{Y}(X|T=1)$ along with estimated individual treatment effects.
Details:
predict()
method inCausalTreeRegressor
andCausalRandomForestRegressor
got flagwith_outcomes
that enables to return nx3 array of outcomes and treatment effects.causal_trees_with_synthetic_data.ipynb
notebook.predict()
outputRelated issues: #590
Update:
numpy
versionpyproject.toml
.Types of changes
What types of changes does your code introduce to CausalML?
Put an
x
in the boxes that applyChecklist
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.Further comments
If this is a relatively large or complex change, kick off the discussion by explaining why you chose the solution you did and what alternatives you considered, etc. This PR template is adopted from appium.