From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies

TL;DR

This paper introduces a causal framework for developing patient-specific treatment recommendations from observational data, emphasizing safety, validity, and causal identification, demonstrated through a heart failure case study.

Contribution

It offers a practical pipeline integrating existing causal methods for personalized treatment learning, inspired by the target trial paradigm, with a real-world application.

Findings

Pipeline can improve patient outcomes over current treatments

Framework ensures safety and causal validity in observational data

Demonstrated effectiveness in heart failure treatment optimization

Abstract

We propose a framework for building patient-specific treatment recommendation models, building on the large recent literature on learning patient-level causal models and inspired by the target trial paradigm of Hernan and Robins. We focus on safety and validity, including the crucial issue of causal identification when using observational data. We do not provide a specific model, but rather a way to integrate existing methods and know-how into a practical pipeline. We further provide a real world use-case of treatment optimization for patients with heart failure who develop acute kidney injury during hospitalization. The results suggest our pipeline can improve patient outcomes over the current treatment regime.