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Found 19 papers, 6 papers with code
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments.
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
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.
Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial
Accurately inferring the root causes of disease from sequencing data can improve the discovery of novel therapeutic targets.
Counterfactual Formulation of Patient-Specific Root Causes of Disease
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis.
Sample-Specific Root Causal Inference with Latent Variables
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome.
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category.
Identifying Patient-Specific Root Causes of Disease
Complex diseases are caused by a multitude of factors that may differ between patients.
Generalizing Clinical Trials with Convex Hulls
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).
Synthesized Difference in Differences
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.
Automated Hyperparameter Selection for the PC Algorithm
The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I $\alpha$ level.
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large.
Causal Discovery with a Mixture of DAGs
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations.
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias.
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Many real datasets contain values missing not at random (MNAR).
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing.
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
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.
Markov Boundary Discovery with Ridge Regularized Linear Models
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.
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data
However, the proposed algorithm using a CDM outperforms the proposed algorithm using a DM only when sample sizes are above several hundred.
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
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.
