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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Counterfactual Explanations for Arbitrary Regression Models.
TL;DR: In this paper, a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models is presented, with support for arbitrary regression models and constraints like feature sparsity and actionable recourse.
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Targeted VAE: Variational and Targeted Learning for Causal Inference
TL;DR: In this article, the authors combine structured inference and targeted learning for causal inference with observational data, and apply a regularizer derived from the influence curve in order to reduce residual bias.
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Doubly Robust Direct Learning for Estimating Conditional Average Treatment Effect
Haomiao Meng,Xingye Qiao +1 more
TL;DR: In this article, the authors proposed a robust direct learning (RD-Learning) method to estimate the conditional average treatment effect (CATE), i.e., the difference in the conditional mean outcome between treatments given covariates.
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NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments.
TL;DR: Neural Counterfactual Relation Estimation (NCoRE) as mentioned in this paper is a new method for learning counterfactual representations in the combination treatment setting that explicitly models cross-treatment interactions, based on a branched conditional neural representation that includes learnt treatment interaction modulators to infer the potential causal generative process underlying the combination of multiple treatments.
Book ChapterDOI
From Conventional Data Analysis Methods to Big Data Analytics
TL;DR: Data analysis in this chapter mainly means descriptive and exploratory methods, also known as unsupervised, that describe as well as structure a set of data that can be represented in the form of a rectangular table crossing n statistical units and p variables.
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.