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

TL;DR

This paper introduces a split-treatment analysis method that ranks individuals likely to benefit from a prospective intervention using only observational data of a proxy treatment, enabling targeted strategies without prior intervention data.

Contribution

The paper presents a novel split-treatment approach that ranks heterogeneous causal effects without requiring direct observations of the target treatment, relying instead on proxy treatment data.

Findings

Effective ranking of individuals for prospective interventions demonstrated on real-world data.

Method validated through randomized experiments confirming the ranking accuracy.

Applicable to large-scale targeting tasks with observational data.

Abstract

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. Instead it relies on observations of a proxy treatment that is caused by the target treatment. Under reasonable assumptions, we show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect. In the absence of any interventional data for cross-validation, Split-Treatment uses sensitivity analyses for unobserved confounding to select model parameters. We apply Split-Treatment to both a simulated data and a large-scale, real-world targeting task and validate our discovered rankings via a randomized experiment for the latter.