Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?

TL;DR

This paper proposes a comprehensive methodology for multitreatment decision-making that incorporates causal effect estimation, uncertainty measures, and prediction criteria to improve treatment selection, demonstrated through a credit card limit adjustment case study.

Contribution

It introduces a novel approach combining causal effect estimation with uncertainty and prediction criteria for optimal multitreatment selection, addressing limitations of existing methods.

Findings

Incorporating additional criteria improves treatment policy performance.

The methodology ensures the overlap assumption via propensity score modeling.

Application to credit card limits shows enhanced decision quality.

Abstract

When making treatment selection decisions, it is essential to include a causal effect estimation analysis to compare potential outcomes under different treatments or controls, assisting in optimal selection. However, merely estimating individual treatment effects may not suffice for truly optimal decisions. Our study addressed this issue by incorporating additional criteria, such as the estimations' uncertainty, measured by the conditional value-at-risk, commonly used in portfolio and insurance management. For continuous outcomes observable before and after treatment, we incorporated a specific prediction condition. We prioritized treatments that could yield optimal treatment effect results and lead to post-treatment outcomes more desirable than pretreatment levels, with the latter condition being called the prediction criterion. With these considerations, we propose a comprehensive methodology for multitreatment selection. Our approach ensures satisfaction of the overlap assumption, crucial for comparing outcomes for treated and control groups, by training propensity score models as a preliminary step before employing traditional causal models. To illustrate a practical application of our methodology, we applied it to the credit card limit adjustment problem. Analyzing a fintech company's historical data, we found that relying solely on counterfactual predictions was inadequate for appropriate credit line modifications. Incorporating our proposed additional criteria significantly enhanced policy performance.