55 citations · 66 across the 17 of their papers we have counts for
12 papers · 1 filter
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…
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…
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 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…