Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

Understand

For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm.

• However, a key challenge is that no data is available about the effect of such a prospective intervention since it has not been deployed yet.
• In this work, we propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention using past observational data.
• Unlike standard causal inference methods, the split-treatment method does not need any observations of the target treatments themselves.