Policy Learning via Optimal Retargeting: Simulation and Job Counseling Case Study

A methodological paper by Nathan Kallus of Cornell University introduces a retargeting solution for policy learning from observational data. The work includes extensive empirical results from a simulation study and a case study on personalized job counseling. The paper is published on paperswithcode under an Open Access license.

Use Cases

• Improving policy learning procedures in observational studies based on the described retargeting method.
• Extracting individualized treatment regimes for personalized job counseling based on the case study mentioned.
• Mitigating bias from lack of overlap in data for different actions using the characterized optimal reference policy and retargeting weights.
• Evaluating the asymptotic efficient estimation variance of new learning objectives in binary-action and multi-action settings.

Strengths

• Method is supported by extensive empirical results from a simulation study and a case study.
• The paper is published on an academic platform (paperswithcode) and is Open Access.
• The author is affiliated with Cornell University, suggesting academic rigor.

Limitations

• Row count, column definitions, and sample data are unavailable, limiting suitability assessment.
• The dataset's file formats and size are unknown.
• The last update date is unknown; freshness is unverified.

Provenance

Source

paperswithcode

Collection Method

Likely contains simulation data and results from a case study on personalized job counseling, as described in the paper.