31 citations · 93 across the 12 of their papers we have counts for
12 papers
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
Jeff Druce, James Niehaus, Vanessa Moody +2
The advances in artificial intelligence enabled by deep learning architectures are undeniable. In several cases, deep neural network driven models have surpassed human level perfor…
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
Sam Witty, David Jensen, Vikash Mansinghka
A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure. For example, Bayesian approaches have been de…
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
Amanda Gentzel, Purva Pruthi, David Jensen
Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences. A variety of theoretical…
Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty, Kenta Takatsu, David Jensen +1
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects. In some cases, these confounders are shared across obser…
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Katherine A. Keith, David Jensen, Brendan O'Connor
Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that inf…
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Akanksha Atrey, Kaleigh Clary, David Jensen
Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice in…