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
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Journal ArticleDOI
ProPPA: Probabilistic Programming for Stochastic Dynamical Systems
TL;DR: A Probabilistic Programming Process Algebra (ProPPA), the first instance of the probabilistic programming paradigm being applied to a high-level, formal language, and its supporting tool suite is presented.
Proceedings ArticleDOI
Estimating Error and Bias in Offline Evaluation Results
Mucun Tian,Michael D. Ekstrand +1 more
TL;DR: It is found that missing data in the rating or observation process causes the evaluation protocol to systematically mis-estimate metric values, and in some cases erroneously determine that a popularity-based recommender outperforms even a perfect personalized recommender.
Posted Content
Improving Offline Contextual Bandits with Distributional Robustness.
TL;DR: This paper extends the Distributionally Robust Optimization (DRO) framework to introduce a convex reformulation of the Counterfactual Risk Minimization principle, and relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework.
Proceedings Article
Uncovering Main Causalities for Long-tailed Information Extraction
TL;DR: This article propose counterfactual information extraction (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference, which is based on a unified structural causal model (SCM) for various information extraction tasks.
Proceedings Article
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
TL;DR: It is illustrated that, in presence of Markov process model misspecification, counterfactual inference leverages prior data, and therefore estimates the outcome of an intervention more accurately than a direct simulation.
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.