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Optimize Intervention Allocation in A/B Testing

This project is to gauge the individual treatment effect in randomized controlled trials (RCT) using Meta-Learner, a machine learning framework. And then provide advice for next round intervention allocation based on the pattern we found in individual effects. You can read my full description in thesis.pdf.

Affiliation: Institute for Social and Economic Research and Policy, Columbia University.

Keywords: Randomized Controlled Trails (RCT), Meta-Leaner

Software:

  • Python : econml, linearmodels, statsmodels sklearn

Reference

Data and Scope

Note: Data sets are not posted due to privacy and intellectual property concerns. Special thanks to Dr. Don Green grants me the access to the data files.

  • Exploratory analysis on three text-messaging voter mobilization experiments conducted before 2016 general election.

    Experiment Treatment Group Control Group Script
    One Arizona Campaign 200,442 50,187 code/One Arizona Experiment.ipynb
    NextGen Climate Campaign 94,257 94,229 code/NextGen Climate Experiment.ipynb
    Vote.org 905,396 301,920 code/Vote Org Experiment.ipynb
    Pooled 1,019,697 446,336 code/Pooled Analysis.ipynb

  • Estimate individual treatment by regression with interaction term

  • Estimate individual treatment by meta-learner approach

Exploratory analysis

explora

Regression

regression

Meta-Learner

meta