A Causal Machine Learning Framework for Predicting Preventable Hospital Readmissions

by   Ben J. Marafino, et al.

Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies–that is, to enable interventions to be targeted to the patients who will presumably benefit most. Despite taking advantage of recent advances in supervised machine learning, these algorithms remain, in a sense, blunt instruments–often being developed and deployed without a full accounting of the causal aspects of the prediction problems they are intended to solve. Indeed, in many settings, including among patients at risk of readmission, the riskiest patients may derive less benefit from a preventative intervention compared to those at lower risk. Moreover, targeting an intervention to a population, rather than limiting it to a small group of high-risk patients, may lead to far greater overall utility if the patients with the most modifiable (or preventable) outcomes across the population could be identified. Based on these insights, we introduce a causal machine learning framework that decouples this prediction problem into causal and predictive parts, which clearly delineates the complementary roles of causal inference and prediction in this problem. We estimate treatment effects using causal forests, and characterize treatment effect heterogeneity across levels of predicted risk using these estimates. Furthermore, we show how these effect estimates could be used in concert with the modeled "payoffs" associated with successful prevention of individual readmissions to maximize overall utility. Based on data taken from before and after the implementation of a readmissions prevention intervention at Kaiser Permanente Northern California, our results suggest that nearly four times as many readmissions could be prevented annually with this approach compared to targeting this intervention using predicted risk.


page 20

page 24


Implementation of Tripartite Estimands Using Adherence Causal Estimators Under the Causal Inference Framework

Intercurrent events (ICEs) and missing values are inevitable in clinical...

Optimal sizing of a holdout set for safe predictive model updating

Risk models in medical statistics and healthcare machine learning are in...

Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU

Recent advances in causal inference techniques, more specifically, in th...

Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis

The aim of clinical effectiveness research using repositories of electro...

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

We apply causal machine learning algorithms to assess the causal effect ...

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model

Clinical researchers often select among and evaluate risk prediction mod...

Prediction meets causal inference: the role of treatment in clinical prediction models

In this paper we study approaches for dealing with treatment when develo...

Please sign up or login with your details

Forgot password? Click here to reset