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11/12/2022
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
Learning causal structure is useful in many areas of artificial intellig...
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09/19/2022
Measuring Interventional Robustness in Reinforcement Learning
Recent work in reinforcement learning has focused on several characteris...
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06/10/2021
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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02/23/2021
A Simulation-Based Test of Identifiability for Bayesian Causal Inference
This paper introduces a procedure for testing the identifiability of Bay...
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01/14/2021
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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10/06/2020
Using Experimental Data to Evaluate Methods for Observational Causal Inference
Methods that infer causal dependence from observational data are central...
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07/14/2020
Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders—unobserved variables that influence both treatment an...
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05/01/2020
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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12/09/2019
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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10/30/2019
Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
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10/11/2019
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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05/07/2019
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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04/12/2019
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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12/07/2018
Measuring and Characterizing Generalization in Deep Reinforcement Learning
Deep reinforcement-learning methods have achieved remarkable performance...
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09/26/2013
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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06/15/2012