Distributionally Robust Policy Learning with Wasserstein Distance

Understand

The effects of treatments are often heterogeneous, depending on the observable characteristics, and it is necessary to exploit such heterogeneity to devise individualized treatment rules (ITRs).

• Existing estimation methods of such ITRs assume that the available experimental or observational data are derived from the target population in which the estimated policy is implemented.
• However, this assumption often fails in practice because of limited useful data.
• In this case, policymakers must rely on the data generated in the source population, which differs from the target population.