Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

Added fit_args to estimate_effect() that can be used with fit_fast() with the default variable weight algorithm (sklearn's MTLassoCV). Common usages would include increasing max_iter (if you want to improve convergence) or n_jobs (if you want to run in parallel).
Separated CV folds from Cross-fitting folds in estimate_effects(). cv_folds controls the amount of additional estimation done on the control units (this used to be controled by max_n_pl, but that parameter now only governs the amount of post-estimation processing that is done). This will do cv_folds extra estimations per treatment time period, though if =1 then no extra work will be done (but control residuals might be biased toward 0).
Added additional option Y_col_block_size to MTLassoCV_MatchSpace_factory to estimate V on block-averages of Y (e.g. taking a 150 cols down to 5 by doing averages over 30 cols at a time).
Added se_factor to MTLassoCV_MatchSpace_factory to use a different penalty than the MSE min.
For large data, approximate the outcomes using a normal distribution (DescrSet), and allow for calculating estimates.

Added tools too to use fit_fast() with large datasets. This includes the sample_frac option to MTLassoCV_MatchSpace_factory() to estimate the match space on a subset of the observations. It also includes fit_fast() option avoid_NxN_mats which will avoid making large matrices (at the expense of only returning the Synthetic control targets and targets_aux and not the full weight matrix)
Added logging in fit_fast via the verbose numerical option. This can help identify out-of-memory errors.
Added a pseudo-Doubly robust match space maker D_LassoCV_MatchSpace_factory. It apporptions some of the normalized variable V weight to those variables that are good predictors of treatment. This should only be done if there are many treated units so that one can reasonably model this relationship.
Switched using standardized Azure Batch config library

Initial release.