7 citations · 16 across the 6 of their papers we have counts for
7 papers
Sensitivity Analysis of the Consistency Assumption
Brian Knaeble, Qinyun Lin, Erich Kummerfeld +1
Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden…
Prompt Optimization and Evaluation for LLM Automated Red Teaming
Michael Freenor, Lauren Alvarez, Milton Leal +8
Applications that use Large Language Models (LLMs) are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accele…
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts
Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well ch…
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method
Bryan Andrews, Erich Kummerfeld
The number of artificial intelligence algorithms for learning causal models from data is growing rapidly. Most ``causal discovery'' or ``causal structure learning'' algorithms are…
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero +2
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of l…
Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls
Erich Kummerfeld, Jaewon Lim, Xu Shi
Negative control variables are increasingly used to adjust for unmeasured confounding bias in causal inference using observational data. They are typically identified by subject ma…
Investigating the effect of binning on causal discovery
Andrew Colt Deckert, Erich Kummerfeld
Binning (a.k.a. discretization) of numerically continuous measurements is a wide-spread but controversial practice in data collection, analysis, and presentation. The consequences…