BACKGROUND: Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformat...
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 randomi...
In recent years, the well-being impact assessment approach has been applied in the area of Artificial Intelligence (AI). Existing well-being frameworks and tools provide a relevant starting point. Taking into account its multidimensional nature, well...
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue R...
To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders an...
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effec...
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corres...
Journal of the American College of Radiology : JACR
37506964
Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various subgroups limit the usefulness of AI in improving patients' lives. Many ...
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning method...
Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from obser...