Open AccessJournal Article
Counterfactual reasoning and learning systems: the example of computational advertising
Léon Bottou,Jonas Peters,Joaquin Quiñonero-Candela,Denis X. Charles,D. Max Chickering,Elon Portugaly,Dipankar Ray,Patrice Y. Simard,Ed Snelson +8 more
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TLDR
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system and allow both humans and algorithms to select the changes that would have improved the system performance.Abstract:
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.read more
Citations
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Proceedings Article
Deep IV: a flexible approach for counterfactual prediction
TL;DR: This paper provides a recipe for augmenting deep learning methods to accurately characterize causal relationships in the presence of instrument variables (IVs)—sources of treatment randomization that are conditionally independent from the outcomes.
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Causal embeddings for recommendation
Stephen Bonner,Flavian Vasile +1 more
TL;DR: In this article, the authors propose a domain adaptation algorithm that learns from logged data containing outcomes from a biased recommendation policy and predicts recommendation outcomes according to random exposure, which is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy.
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Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
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A Survey of Learning Causality with Data: Problems and Methods
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Proceedings ArticleDOI
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
TL;DR: Using simulations, it is demonstrated how using data confounded in this way homogenizes user behavior without increasing utility.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.