
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
The advances in artificial intelligence enabled by deep learning archite...
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A SimulationBased Test of Identifiability for Bayesian Causal Inference
This paper introduces a procedure for testing the identifiability of Bay...
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Preserving Privacy in Personalized Models for Distributed Mobile Services
The ubiquity of mobile devices has led to the proliferation of mobile se...
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Using Experimental Data to Evaluate Methods for Observational Causal Inference
Methods that infer causal dependence from observational data are central...
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Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders—unobserved variables that influence both treatment an...
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Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates
Many applications of computational social science aim to infer causal co...
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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL
Saliency maps have been used to support explanations of deep reinforceme...
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Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
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The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Causal inference is central to many areas of artificial intelligence, in...
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Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
Evaluation of deep reinforcement learning (RL) is inherently challenging...
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Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Reproducibility in reinforcement learning is challenging: uncontrolled s...
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Measuring and Characterizing Generalization in Deep Reinforcement Learning
Deep reinforcementlearning methods have achieved remarkable performance...
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A Sound and Complete Algorithm for Learning Causal Models from Relational Data
The PC algorithm learns maximally oriented causal Bayesian networks. How...
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Identifying Independence in Relational Models
The rules of dseparation provide a framework for deriving conditional i...
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David Jensen
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