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
Reads0
Chats0
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
More filters
Posted Content
Balanced Policy Evaluation and Learning
TL;DR: A new, balance-based approach that makes the data look like the new policy but does so directly by finding weights that optimize for balance between the weighted data and the target policy in the given, finite sample, which is equivalent to minimizing worst-case or posterior conditional mean square error.
Posted Content
Causal Inference in Network Economics.
TL;DR: In this article, the authors explore causal inference in network economics, building on the mathematical framework of variational inequalities, which is a generalization of classical optimization and can be viewed as a synthesis of the well-known variational inequality formalism with the broad principles of causal inference.
Posted Content
Bandit Algorithms for Precision Medicine
Yangyi Lu,Ziping Xu,Ambuj Tewari +2 more
TL;DR: The role of bandit algorithms in precision medicine has been reviewed by Tewari and Murphy, 2017; Rabbi et al. as mentioned in this paper, 2019; and several new topics have emerged in the research on bandit algorithm.
Posted Content
Control Variates for Slate Off-Policy Evaluation
TL;DR: In this article, the authors study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates, and obtain new estimators with risk improvement guarantees over both the pseudoinverse estimator and self-normalized estimator.
Journal ArticleDOI
An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach
TL;DR: A causal inference framework is provided to model the effects of machine learning algorithms on user preferences and uses this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner.
References
More filters
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.