A Causal Machine Learning Framework for Estimating the Impact of Cancer Diagnosis on Receipt of Advance Care Planning.

Abstract

Objective: Develop a causal machine learning (causal ML) framework for estimating how a diagnosis (cancer in this study) affects the likelihood of receiving a specific health care service (advance care planning in this study) and associated heterogeneity.

Study setting and design: Our proposed framework leverages the causal forest method, combined with a population-weighted resampling and averaging over estimations strategy, to estimate average treatment effects (ATEs) and conditional average treatment effects (CATEs). Post hoc, we used best linear projections to identify covariates associated with variation in the CATEs. We illustrate the framework by applying it to a stratified random sample of patients, where the strata are defined by the crosstabulation of cancer diagnosis (diagnosed vs. not diagnosed) and ACP receipt (documented vs. not documented).

Data sources and analytic sample: We extracted deidentified patient data from October 2019 to October 2024 (n = 87,772) with explanatory variables in three categories: demographics, morbidity, and health care system utilization.

Principal findings: In application of the causal ML framework, we found that patients diagnosed with cancer at this health care system to be at least 17.2% more likely to have documented ACP than similar patients not diagnosed with cancer. We also found significant heterogeneity. For instance, a one standard deviation increase in in-person outpatient visits was associated with an on-average increase in the CATE estimate (by 6.1 percentage points), while a one standard deviation increase in hospital admissions, inpatient days, and surgical duration in minutes was associated with an on-average decrease in the CATE estimate (by -1.3, -5.6, and -0.5 percentage points, respectively).

Conclusions: The proposed causal ML framework enables estimation of the effect of a diagnosis on receiving a relevant health care service. In the cancer diagnosis context, it can identify patient groups less likely to receive ACP, thus informing service allocation strategies.