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Stochastic Treatment TMLE #52
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A heuristic for how to approach IPW estimation for stochastic treatments. (1) Weight the population to the all vs none comparisons (standard procedure), then (2) reweight again to the intervention targeted. |
General reminder to myself: stochastic treatment TMLE is no longer a one-step fit. There has to be an interative process of estimating Q* until convergence criteria is met. This means repeating the targeting step several times. |
For comparisons, I can use R's |
Added as part of 0.8.2 |
This is another valuable addition to TMLE (that I also need as part of a project I am working on). Essentially, this would allow more complex treatments than treat-all vs. treat-none, similar to custom treatments in the g-formula.
What it does is shift the probability of A distribution. However, the single-step convergence of TMLE is no longer valid. I would need to iteratively estimate Q* until it epsilon converges to 0. This should be easy enough. After convergence, follows the remainder of the TMLE procedure
Likely best if I make this separate from
TMLE
Starting points;
https://github.com/tlverse/tmle3shift
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4117410/#SD1
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