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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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
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Hi-CI: Deep Causal Inference in High Dimensions
TL;DR: The proposed Hi-CI network, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables, is proposed and the efficacy of causal effect prediction of the proposed network is demonstrated using synthetic and real-world NEWS datasets.
Book ChapterDOI
The Upper Confidence Bound Algorithm: Minimax Optimality
Tor Lattimore,Csaba Szepesvári +1 more
Discovering Data-Driven Actionable Intelligence for Clinical Decision Support
TL;DR: This dissertation addresses the question of how machine learning techniques can capitalize on these data resources to assist clinicians in predicting, preventing and treating illness by developing a set of MLbased, data-driven models of patient outcomes that are embedded within systems of decision support deployed at different stages of patient care.
Posted Content
Feedback Detection for Live Predictors
TL;DR: In this article, the authors analyze predictor feedback detection as a causal inference problem, and introduce a local randomization scheme that can be used to detect non-linear feedback in real-world problems.
Proceedings ArticleDOI
On Shannon capacity and causal estimation
Rahul Kidambi,Sreeram Kannan +1 more
TL;DR: The utility of Shannon capacity as a metric for causal directionality estimation is proposed and studied and opens up several open questions and directions for future study.
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.