Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation.

A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. In healthcare, the gold standard for causal effect measurements is randomized controlled trials (RCTs), in which a target population is explicitly defined and each study sample is randomly assigned to either the treatment or control cohorts. The great potential to derive actionable insights from causal relationships has led to a growing body of machine-learning research applying causal effect estimators to observational data in the fields of healthcare, education, and economics. The primary difference between causal effect studies utilizing observational data and RCTs is that for observational data, the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism. This can lead to massive differences in covariate distributions between control and treatment samples, making a comparison of causal effects confounded and unreliable. Classical approaches have sought to solve this problem piecemeal, first by predicting treatment assignment and then treatment effect separately. Recent work extended part of these approaches to a new family of representation-learning algorithms, showing that the upper bound of the expected treatment effect estimation error is determined by two factors: the outcome generalization-error of the representation and the distance between the treated and control distributions induced by the representation. To achieve minimal dissimilarity in learning such distributions, in this work we propose a specific auto-balancing, self-supervised objective. Experiments on real and benchmark datasets revealed that our approach consistently produced less biased estimates than previously published state-of-the-art methods. We demonstrate that the reduction in error can be directly attributed to the ability to learn representations that explicitly reduce such dissimilarity; further, in case of violations of the positivity assumption (frequent in observational data), we show our approach performs significantly better than the previous state of the art. Thus, by learning representations that induce similar distributions of the treated and control cohorts, we present evidence to support the error bound dissimilarity hypothesis as well as providing a new state-of-the-art model for causal effect estimation.

Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes.

Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4th of the total healthcare spending in the United States (US). Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various combinations. Strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million high-risk diabetic patients (HbA1c ≥ 9%) in the US is analyzed to evaluate the comparative effectiveness for HbA1c reduction in this population of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical cohorts defined by age, insulin dependence, and a number of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized evaluation of treatment selection. An average confounder-adjusted reduction in HbA1c of 0.69% [-0.75, -0.65] is observed between patients receiving high vs low ranked treatments across cohorts for which the difference was significant. This method can be extended to explore treatment optimization for other chronic conditions.

MAgEC: Using Non-Homogeneous Ensemble Consensus for Predicting Drivers in Unexpected Mechanical Ventilation.

We conduct exploratory analysis of a novel algorithm called Model Agnostic Effect Coefficients (MAgEC) for extracting clinical features of importance when assessing an individual patient's healthcare risks, alongside predicting the risk itself. Our approach uses a non-homogeneous consensus-based algorithm to assign importance to features, which differs from similar approaches, which are homogeneous (typically purely based on random forests). Using the MIMIC-III dataset, we apply our method on predicting drivers/causers of unexpected mechanical ventilation in a large cohort patient population. We validate the MAgEC method using two primary metrics: its accuracy in predicting mechanical ventilation and the similarity of the proposed feature importances to a competing algorithm (SHAP). We also more closely discuss MAgEC itself by examining the stability of our proposed feature importances under different perturbations and whether the non-homogeneity of the approach actually leads to feature importance diversity. The code to implement MAgEC is open-sourced on GitHub (https://github.com/gstef80/MAgEC).