All notable changes to maq
will be documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
- Add
get_aipw_scores
for calculating AIPW scores given user-supplied estimates, and add some minor polish toget_ipw_scores
. #72, #71
- Draw a horizontal line when constructing a new plot depending on user-supplied xlim. #73
- Fix a minor discrepancy in
integrated_difference
. #48
- Fix a bug in
integrated_difference
where the AUC measure is wrong if \bar B exceeds the point at which the curve plateaus. #44
- Change the
maq
function signature to makebudget
an optional argument. The default behavior (budget = NULL
) is to fit the Qini curve up to a maximum spend/unit where each unit that is expected to benefit, is treated. #41
- Add
integrated_difference(object.lhs, object.rhs, spend)
for estimating the area between two Qini curves up to some maximum budgetspend
. #42
- Add a
type = c("matrix", "vector")
option topredict.maq
, optionally returning predictions in the set {0, 1, ..., K} iftype = "vector"
. #29 - Add a convenience function
get_ipw_scores
to construct evaluation scores via IPW. #28 - Add more documentation on statistical details from the paper. #27
- Have
predict.maq
return a standard dense matrix, and remove dependence on the sparse Matrix package. #30
- Fix
horizontal.line
in plot, the option for whether a curve added to the plot should extend all the way to the right of a main plot, if the added Qini curve's spend path stops before the spend path of the main plot.
First CRAN beta release (this changelog tracks the R package). The R package currently supports
- Fitting Qini curves for an arbitrary number of arms using
maq(cate.hat, cost.hat, max.budget, Y.eval, ...)
, wherecate.hat
andcost.hat
are CATE and cost estimates obtained via some function learned on a training set,Y.eval
are evaluation scores on a test set (for example inverse-propensity weighted outcomes) andmax.budget
is the maximum mean budget/unit to fit the curve on. Setting the optiontarget.with.covariates
toFALSE
yields a baseline Qini curve that can be used to assess the value of treatment targeting based on covariates. - Computing point estimates and standard errors for any point on the curve with
average_gain()
(supporting clustered standard errors if fit with clusters). - Comparing arbitrary points on different curves using
difference_gain()
, yielding standard errors that account for the correlation arising from curves fit on the same evaluation data. - Retrieving the underlying "induced" policy at arbitrary spend levels with
predict()
. - Basic plotting functionality with
plot()
. - Retrieving the full gain/spend/allocation path with
summary(maq.object)
.
The Python bindings (source install only) currently supports
- Fitting the Qini curve, and retrieving standard errors for any point on the curve, as well as the implied policy \pi (remaining functionality can be added on the wrapper side - the heavy lifting is done in core/C++, and the input/output is language agnostic).