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Deep Learning of Potential Outcomes
- Publication Year :
- 2021
-
Abstract
- This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
bepress|Social and Behavioral Sciences|Economics
SocArXiv|Social and Behavioral Sciences|Sociology|Methodology
Econometrics (econ.EM)
bepress|Social and Behavioral Sciences|Political Science|Models and Methods
Machine Learning (stat.ML)
SocArXiv|Social and Behavioral Sciences|Political Science
Machine Learning (cs.LG)
Methodology (stat.ME)
FOS: Economics and business
bepress|Social and Behavioral Sciences|Political Science
SocArXiv|Social and Behavioral Sciences|Sociology
Statistics - Machine Learning
bepress|Social and Behavioral Sciences|Social Statistics
bepress|Social and Behavioral Sciences|Economics|Econometrics
Statistics - Methodology
Economics - Econometrics
SocArXiv|Social and Behavioral Sciences|Economics
SocArXiv|Social and Behavioral Sciences|Social Statistics
bepress|Social and Behavioral Sciences|Sociology
SocArXiv|Social and Behavioral Sciences|Political Science|Models and Methods
SocArXiv|Social and Behavioral Sciences|Economics|Econometrics
bepress|Social and Behavioral Sciences|Sociology|Quantitative, Qualitative, Comparative, and Historical Methodologies
bepress|Social and Behavioral Sciences
SocArXiv|Social and Behavioral Sciences
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....2a53cd398d1a8e2ee5f399e5607aa8ed