Multi-epsilon fluctuation, data-adaptive truncation, and the CCW-OSE#148
Open
joshua-slaughter wants to merge 7 commits intomainfrom
Open
Multi-epsilon fluctuation, data-adaptive truncation, and the CCW-OSE#148joshua-slaughter wants to merge 7 commits intomainfrom
joshua-slaughter wants to merge 7 commits intomainfrom
Conversation
…ge in fluctuation leads to slightly different estimates; change greedy and adaptive tests as different confounders are now selected
joshua-slaughter
added
bug
Open
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This PR aims to introduce changes to align TMLE.jl with other TMLE software implementations through the implementation of targeted the counterfactual means through the use of multiple fluctuation parameters$\epsilon = (\epsilon_0, \epsilon_1, ... )$ . This implementation will also enable the ability to develop targeted estimators for the marginal odds ratio and risk ratio in future updates.
Data-adaptive truncation was an optional feature and how now been updated to be the default as it provides the optimal bias-variance tradeoff in finite samples.
Furthermore, all prevalence weights are now normalised for stability upon entering the fluctuation and initial fits.
Additionally, I have also added a simple CCW-OSE that show similar large sample behaviour to the CCW-TMLE implementation.