An Automated Approach to Causal Inference in Discrete Settings.

Applied research conditions often make it impossible to point-identify causal estimands without untenable assumptions. -bounds on the range of possible solutions-is a principled alternative, but the difficulty of deriving bounds in idiosyncratic settings has restricted its application. We present a general, automated numerical approach to causal inference in discrete settings.

A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy.

Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone.

Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure.

Background: Despite technological and treatment advancements over the past 2 ​decades, cardiogenic shock (CS) mortality has remained between 40% and 60%. Our objective was to develop an algorithm that can continuously monitor heart failure patients and partition them into cohorts of high and low risk for CS.

Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Health System.

Deriving Bounds and Inequality Constraints Using Logical Relations Among Counterfactuals.

Causal parameters may not be point identified in the presence of unobserved confounding. However, information about non-identified parameters, in the form of bounds, may still be recovered from the observed data in some cases. We develop a new general method for obtaining bounds on causal parameters using rules of probability and restrictions on counterfactuals implied by causal graphical models.