Often a randomized experiment is not economical, and so the only data available is data collected according to some *targeting policy*. In other words, you vary probability of treatment $P(W=1)$, often according either to some business logic or a model output. As long as all individuals are targeted with some non-zero probability, it is still possible to train an unbiased model using this data by simply weighting calculations on each individual according to $P(W=1)$ .

In its current state, pylift supports this kind of correction to an extent. We have added the ability to correct the Qini-style evaluation curves according to a treatment policy (simply add an argument `p`

, defined as $P(W=1)$. We've also adjusted the transformation to allow the policy information to be encoded in the transformation (`pylift.methods.derivatives.TransformedOutcome._transform_func`

). By specifying a column string that in the keyword argument `col_policy`

that specifies the row-level probability of treatment, this encoding is automatically created.

It is therefore possible to write a custom objective function that recovers `treatment`

, `outcome`

, and `policy`

information from the transformed outcome (using the `TransformedOutcome._untransform_func`

function), then adapting the objective function (if possible -- this works with `xgboost`

, but not `sklearn`

) accordingly. However, we have not yet explicitly implemented this custom objective function.