Confounder adjusting before applying the ITE model to observational data
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Description
When we are using observational data for trial emulation and estimating the ATE, we use techniques like PSM or IPTW on the observational data to mimic the randomization in a randomized clinical trial for confounder adjusting.
When we are doing ITE, do we need to conduct a similar effort to get a matched/IPTW-adjusted dataset before we fit the data into the ITE model (e.g., meta learner)?
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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
No files, tests, or entry points are named. Start by reviewing the repository documentation or examples covering observational data, PSM, IPTW, and meta learners. Done means adding a clear, referenced explanation of whether and how confounder adjustment should be handled before fitting an ITE model.
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Assessment
- Tech stack
- machine-learning, python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100