Counterfactual Inference with Hidden Confounders Using Implicit Generative Models

Fujin Zhu; Adi Lin; Guangquan Zhang; Jie Lü

doi:10.1007/978-3-030-03991-2_47

ScienceGate Book Chapters

BOOK-CHAPTER

Counterfactual Inference with Hidden Confounders Using Implicit Generative Models

Fujin Zhu Adi Lin Guangquan Zhang Jie Lü

Year: 2018 Lecture notes in computer science Pages: 519-530 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-03991-2_47

Get Full-Text PDF Get Analytical Report

Keywords:

Latent variable Inference Computer science Counterfactual thinking Machine learning Artificial intelligence Bayesian inference Latent variable model Generative model Outcome (game theory) Econometrics Generative grammar Bayesian probability Mathematics Mathematical economics

Metrics

Cited By

2.59

FWCI (Field Weighted Citation Impact)

Refs

0.90

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Causal Inference Techniques

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Machine Learning and Algorithms

Physical Sciences → Computer Science → Artificial Intelligence

Counterfactual Inference with Hidden Confounders Using Implicit Generative Models

Metrics

Citation History

Topics

Related Documents

Deep generative models for time series counterfactual inference

Double debiased estimation and inference for longitudinal generalized linear models with hidden confounders

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Avoiding the Unconfoundednes Assumption: Counterfactual Inference Considering Unobserved Confounders

Conformal Counterfactual Inference under Hidden Confounding