> For the complete documentation index, see [llms.txt](https://docs.pylift.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pylift.org/policy.md).

# Usage: custom targeting policy

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.