Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. Within the Neyman-Rubin potential outcomes model, we use...

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Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 12; H. 5; S. 1031 - 1046
Hauptverfasser:	Alaa, Ahmed M., van der Schaar, Mihaela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Asymptotic methods Bayes methods Bayesian analysis Bayesian nonparametrics Bias causal effect inference Data models Divergence Domains Empirical analysis Estimation Gaussian process Gaussian processes Hilbert space Inference Inference algorithms Information rates Information theory Machine learning Maximization multitask learning Nonparametric statistics Optimization selection bias Signal processing algorithms Smoothness
ISSN:	1932-4553, 1941-0484
Online-Zugang:	Volltext
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