no code implementations • 8 Feb 2024 • Thomas A. Lasko, John M. Still, Thomas Z. Li, Marco Barbero Mota, William W. Stead, Eric V. Strobl, Bennett A. Landman, Fabien Maldonado
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments.
no code implementations • 8 Nov 2023 • Thomas A. Lasko, Eric V. Strobl, William W. Stead
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites.
no code implementations • 10 Jul 2023 • Eric V. Strobl
Accurately inferring the root causes of disease from sequencing data can improve the discovery of novel therapeutic targets.
no code implementations • 27 May 2023 • Eric V. Strobl
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis.
no code implementations • 27 Oct 2022 • Eric V. Strobl, Thomas A. Lasko
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome.
no code implementations • 25 May 2022 • Eric V. Strobl, Thomas A. Lasko
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category.
1 code implementation • 23 May 2022 • Eric V. Strobl, Thomas A. Lasko
Complex diseases are caused by a multitude of factors that may differ between patients.
1 code implementation • 25 Nov 2021 • Eric V. Strobl, Thomas A. Lasko
This assumption allows us to extrapolate results from exclusive trials to the broader population by analyzing observational and trial data simultaneously using an algorithm called Optimum in Convex Hulls (OCH).
no code implementations • 2 May 2021 • Eric V. Strobl, Thomas A. Lasko
We instead propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data.
no code implementations • 3 Nov 2020 • Eric V. Strobl
The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I $\alpha$ level.
1 code implementation • 24 May 2019 • Eric V. Strobl, Shyam Visweswaran
Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large.
no code implementations • 28 Jan 2019 • Eric V. Strobl
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations.
1 code implementation • 5 May 2018 • Eric V. Strobl
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias.
no code implementations • 25 May 2017 • Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Many real datasets contain values missing not at random (MNAR).
no code implementations • 13 Feb 2017 • Eric V. Strobl, Kun Zhang, Shyam Visweswaran
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing.
1 code implementation • 14 Jul 2016 • Eric V. Strobl, Peter L. Spirtes, Shyam Visweswaran
The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG.
no code implementations • 14 Sep 2015 • Eric V. Strobl, Shyam Visweswaran
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response.
1 code implementation • 28 Jul 2014 • Eric V. Strobl, Shyam Visweswaran
However, the proposed algorithm using a CDM outperforms the proposed algorithm using a DM only when sample sizes are above several hundred.
no code implementations • 1 Feb 2014 • Eric V. Strobl, Shyam Visweswaran
Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences.