Currently, predict returns the underlying allocation for the policy estimated (and evaluated) on the test set.
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Another use case arises when we have new CATEs and cost and need to predict treatment allocations for these samples under a given budget constraint. In this case, a fitted maq object could use the estimated slope $\rho$ cutoff value $\lambda$ from the test set to determine and return the appropriate allocation (e.g., use the thresholding policy in Theorem 1 here https://arxiv.org/abs/2306.11979)
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Another useful thing to return would be the prioritization scores $\rho$
Doing this requires storing the $\lambda$'s as part of the C++ solver's solution path. That could also be a convenient thing to return as part of the maq results.
Currently,
predictreturns the underlying allocation for the policy estimated (and evaluated) on the test set.Another use case arises when we have new CATEs and cost and need to predict treatment allocations for these samples under a given budget constraint. In this case, a fitted$\rho$ cutoff value $\lambda$ from the test set to determine and return the appropriate allocation (e.g., use the thresholding policy in Theorem 1 here https://arxiv.org/abs/2306.11979)
maqobject could use the estimated slopeAnother useful thing to return would be the prioritization scores$\rho$
Doing this requires storing the$\lambda$ 's as part of the C++ solver's solution path. That could also be a convenient thing to return as part of the maq results.