55 citations · 66 across the 17 of their papers we have counts for
24 papers
COMPACT: Spectral Adjustment Scores from a Complete and Irreducible Causal Criterion
Eric V. Strobl
Observational datasets frequently contain many baseline variables, yet investigators estimating causal effects may not know which variables to include in the adjustment set. Confou…
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
Marco Barbero-Mota, Eric V. Strobl, John M. Still +2
We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health record…
A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records
Marco Barbero Mota, John M. Still, Jorge L. Gamboa +4
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sour…
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Thomas A. Lasko, John M. Still, Thomas Z. Li +5
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it m…
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
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 sit…
Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial
Eric V. Strobl
Accurately inferring the root causes of disease from sequencing data can improve the discovery of novel therapeutic targets. However, existing root causal inference algorithms requ…
Counterfactual Formulation of Patient-Specific Root Causes of Disease
Eric V. Strobl
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathema…
Sample-Specific Root Causal Inference with Latent Variables
Eric V. Strobl, Thomas A. Lasko
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exog…
Identifying Patient-Specific Root Causes of Disease
Eric V. Strobl, Thomas A. Lasko
Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many…
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
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. A few underlying root causes may nevertheless initi…
Generalizing Clinical Trials with Convex Hulls
Eric V. Strobl, Thomas A. Lasko
Randomized clinical trials eliminate confounding but impose strict exclusion criteria that limit recruitment to a subset of the population. Observational datasets are more inclusiv…
Synthesized Difference in Differences
Eric V. Strobl, Thomas A. Lasko
We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) elimina…
Automated Hyperparameter Selection for the PC Algorithm
Eric V. Strobl
The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I level. PC is however unsupervised, so we cannot tune using…
The Global Markov Property for a Mixture of DAGs
Eric V. Strobl
Real causal processes may contain feedback loops and change over time. In this paper, we model cycles and non-stationary distributions using a mixture of directed acyclic graphs (D…
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
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. Most investigators estimate such perso…
Causal Discovery with a Mixture of DAGs
Eric V. Strobl
Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose t…
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Eric V. Strobl
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm ca…
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before…
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Eric V. Strobl, Kun Zhang, Shyam Visweswaran
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one…
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
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 estimat…
Markov Boundary Discovery with Ridge Regularized Linear Models
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 di…
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data
Eric V. Strobl, Shyam Visweswaran
Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods di…
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
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. Several algor…
Deep Multiple Kernel Learning
Eric Strobl, Shyam Visweswaran
Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not general…