OPL_MA: Stata module for multi-action optimal policy learning

Giovanni Cerulli

Statistical Software Components from Boston College Department of Economics

Abstract: opl_ma_fb implements first-best Optimal Policy Learning (OPL) algorithm to estimate the best treatment assignment given an outcome and a set of observed covariates and treatment effects. It allows for different risk preferences in decision-making (i.e., risk-neutral, risk-averse linear, risk-averse quadratic). This command uses linear regression for estimating nuisance conditional means. opl_ma_vf estimates the value-function for multi-action Optimal Policy Learning via three different methods: 1. Regression Adjustment (RA): estimates expected outcomes for each action using regression models. 2. Inverse Probability Weighting (IPW): uses estimated propensity scores to reweigh observations. 3. Doubly Robust (DR): combines RA and IPW for a more robust estimator.

Language: Stata
Requires: Stata version 16
Keywords: optimal; policy; learning (search for similar items in EconPapers)
Date: 2025-10-07
Note: This module should be installed from within Stata by typing "ssc install opl_ma". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/o/opl_ma_fb.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_ma_fb.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_ma_vf.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_ma_vf.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_best_treat.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_best_treat.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_plot_best.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/o/opl_plot_best.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/s/spmdata.dta sample data file (application/x-stata)

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