-
Notifications
You must be signed in to change notification settings - Fork 79
Add pre-test post-test non equivalent group design details to the Knowledge Base #517
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
Conversation
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #517 +/- ##
=======================================
Coverage 95.19% 95.19%
=======================================
Files 28 28
Lines 2457 2457
=======================================
Hits 2339 2339
Misses 118 118 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
Actually, is it worth mentioning more backdoor paths:
|
Maybe I need not mention ANCOVA specifically. Just say 'linear model'. |
Just noticed... what I currently have listed as backdoor path 3 is of course easily closed by observing |
|
@@ -16,7 +16,7 @@ | |||
}, |
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.
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.
It's a case of conditional ignorability....
@@ -16,7 +16,7 @@ | |||
}, |
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.
I wonder if it's worth pivoting out of this example and saying something now about identification in general in causal inference. This ANCOVA is a strong example... to jump off and discuss how all the strategies above are identification strategies.
Reply via ReviewNB
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.
Looks good to me. Added a note about maybe generalising your discussion of identification - leaping off the ANCOVA example to reflect on the set of DAGs above... but just a suggestion. Not blocking for this PR...
We were previously missing this. And I was also looking at the ANCOVA page and realised it needed some work. This PR lays some groundwork for improving the ANCOVA docs/page, but doesn't touch it yet. So far, we just add info into the knowledge base docs page.
Note: This PR also updates the dependency from
daft
todaft-pgm
. This is to keep up with a change of the package name on pypi.Very happy to get any corrections, or feedback to improve clarity or accuracy. I think that's particularly important in this case because I made up this DAG myself - it doesn't feature in any of the (many) causal inference books I have. There's a chance a DAG has been put together in a published paper that I've not seen, so happy to hear about that if you know of such a paper?
📚 Documentation preview 📚: https://causalpy--517.org.readthedocs.build/en/517/