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Adding polle package #17

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8 changes: 7 additions & 1 deletion CausalInference.md
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Expand Up @@ -247,7 +247,13 @@ treatment effect (HTE) estimation.
estimation of individualized treatment rules from observational and
randomized data with options for variable-selection and gradient boosting
based estimation, and for outcome model augmentation (for continuous,
binary, count, and time-to-event outcomes).
binary, count, and time-to-event outcomes).
`r pkg("polle")` provides a unified framework for
learning and evaluating finite stage policies based on observational data
with methods such as doubly robust restricted Q-learning, policy tree
learning, and outcome weighted learning. Flexible machine learning methods
can be used to estimate the nuisance components and valid inference for the
policy value is ensured via cross-fitting.
- Estimation of DTR with *variable selection* is proposed by
`r pkg("ITRLearn")` implements maximin-projection learning
for recommending a meaningful and reliable individualized treatment
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