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OPL_MA: Stata module for multi-action optimal policy learning

Author

Listed:
  • Giovanni Cerulli

    (IRCrES-CNR)

Programming Language

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.

Suggested Citation

  • Giovanni Cerulli, 2025. "OPL_MA: Stata module for multi-action optimal policy learning," Statistical Software Components S459524, Boston College Department of Economics.
  • Handle: RePEc:boc:bocode:s459524
    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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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_ma_fb.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_ma_fb.sthlp
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_ma_vf.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_ma_vf.sthlp
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_best_treat.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_best_treat.sthlp
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_plot_best.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/o/opl_plot_best.sthlp
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    File URL: http://fmwww.bc.edu/repec/bocode/s/spmdata.dta
    File Function: sample data file
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