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CHANGELOG.md

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Changelog

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.

[0.5.0] - 2024-11-14

Added

  • Invisibly return data when calling plot to allow for optional customization with other plotting libraries. #94

Fixed

  • Omit empty CI plots lines when R = 0. #76

[0.4.0] - 2024-04-18

Added

  • Add get_aipw_scores for calculating AIPW scores given user-supplied estimates, and add some minor polish to get_ipw_scores. #72, #71

Fixed

  • Draw a horizontal line when constructing a new plot depending on user-supplied xlim. #73
  • Fix a minor discrepancy in integrated_difference. #48

[0.3.1] - 2023-10-08

Fixed

  • Fix a bug in integrated_difference where the AUC measure is wrong if \bar B exceeds the point at which the curve plateaus. #44

[0.3.0] - 2023-09-10

Changed (breaking)

  • Change the maq function signature to make budget 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

Added

  • Add integrated_difference(object.lhs, object.rhs, spend) for estimating the area between two Qini curves up to some maximum budget spend. #42

[0.2.0] - 2023-09-03

Added

  • Add a type = c("matrix", "vector") option to predict.maq, optionally returning predictions in the set {0, 1, ..., K} if type = "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

Changed

  • Have predict.maq return a standard dense matrix, and remove dependence on the sparse Matrix package. #30

Fixed

  • 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.

[0.1.0] - 2023-06-27

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, ...), where cate.hat and cost.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) and max.budget is the maximum mean budget/unit to fit the curve on. Setting the option target.with.covariates to FALSE 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).