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 ArticleDOI
Introduction to Bandits in Recommender Systems
TL;DR: The aim of this tutorial is to provide a brief introduction to the bandit problem with an overview of the various applications of bandit algorithms in recommendation.
Posted ContentDOI
Learning and Forgetting Using Reinforced Bayesian Change Detection
Vincent Moens,Alexandre Zénon +1 more
TL;DR: A model of behavioural automatization that is based on adaptive forgetting that encompasses many aspects of Reinforcement Learning (RL), such as Temporal Difference RL and counterfactual learning, and accounts for the reduced computational cost of automatic behaviour.
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CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning.
TL;DR: CADET (short for Causal Debugging Toolkit) is proposed that enables users to identify, explain, and fix the root cause of non-functional faults early and in a principled fashion and compares with state-of-the-art configuration optimization and ML-based debugging approaches.
Posted Content
Policy Evaluation with Latent Confounders via Optimal Balance
Andrew Bennett,Nathan Kallus +1 more
TL;DR: It is shown that unlike the unconfounded case no single set of weights can give unbiased evaluation for all outcome models, yet a new algorithm is proposed that can still provably guarantee consistency by instead minimizing an adversarial balance objective.
Posted Content
Causal Deep Information Bottleneck.
TL;DR: This work proposes estimating the causal effect from the perspective of the information bottleneck principle by explicitly identifying a low-dimensional representation of latent confounding, and proves theoretically that the proposed model can be used to recover the average causal effect.
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.