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
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
TL;DR: Li et al. as discussed by the authors proposed a debiased visual-aware recommender system, denoted as CausalRec, to effectively retain the supportive significance of the visual information and remove the visual bias.
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Causal Discovery Using Proxy Variables
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Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
TL;DR: The authors proposed a discriminative framework for estimating the performance of a new treatment given a large dataset of the control condition and data from a small (and possibly unrepresentative) randomized trial comparing new and old treatments.
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Conservative Exploration in Reinforcement Learning
TL;DR: This paper introduces the notion of conservative exploration for average reward and finite horizon problems, and presents two optimistic algorithms that guarantee (w.h.p.) that the conservative constraint is never violated during learning.
Journal ArticleDOI
CausaLM: Causal Model Explanation Through Counterfactual Language Models
TL;DR: The authors propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models, which is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem.
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.