# Using Propensity Scores to Develop and Evaluate Treatment Rules with Observational Data

## TL;DR

This paper presents a flexible method for estimating individualized treatment rules from observational data using propensity scores, addressing challenges of unmeasured confounders and providing tools for evaluation.

## Contribution

It introduces a novel estimation framework that leverages observation weights based on propensity scores, applicable to various prediction methods, and offers a way to assess the population benefit of treatment rules.

## Key findings

- Framework accommodates any prediction method with observation weights
- Provides a method to estimate population benefit of treatment rules
- Implementation available in R package DevTreatRules

## Abstract

In this paper, we outline a principled approach to estimate an individualized treatment rule that is appropriate for data from observational studies where, in addition to treatment assignment not being independent of individual characteristics, some characteristics may affect treatment assignment in the current study but not be available in future clinical settings where the estimated rule would be applied. The estimation framework is quite flexible and accommodates any prediction method that uses observation weights, where the observation weights themselves are a ratio of two flexibly estimated propensity scores. We also discuss how to obtain a trustworthy estimate of the rule's population benefit based on simple propensity-score-based estimators of average treatment effect. We implement our approach in the R package DevTreatRules and share the code needed to reproduce our results on GitHub.