CITCOM-project / CITCOM-project/CausalTestingFramework
Identification of non-minimal adjustment sets
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- Dominant language
- Python
- Stars
- 20
- Forks
- 7
- PR merge metrics
- No merged PRs in 30d
Description
Our current identification algorithm only enables the user to automatically identify minimal adjustment sets (i.e. the smallest set of variables that blocks all back-door paths).
While this is usually preferable (less adjustments generally means a simpler statistical model), there will be some cases where a user wants to use a specific non-minimal adjusment set.
To this end, we should add a function to the CausalDAG class that obtains a list/set of all adjustment sets.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the CausalDAG class and reviewing the existing identification logic for minimal adjustment sets. Trace how current adjustment sets are represented and returned, then define completion as returning all valid adjustment sets, including non-minimal sets, for the requested graph. No specific files or tests are named in the issue.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100