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Structured Neural Networks for Density Estimation and Causal Inference

Structured Neural Networks for Density Estimation and Causal Inference

3 November 2023
Asic Q. Chen
Ruian Shi
Xiang Gao
Ricardo Baptista
Rahul G. Krishnan
    CML
    TPM
ArXivPDFHTML

Papers citing "Structured Neural Networks for Density Estimation and Causal Inference"

5 / 5 papers shown
Title
Normalizing Flows for Interventional Density Estimation
Normalizing Flows for Interventional Density Estimation
Valentyn Melnychuk
Dennis Frauen
Stefan Feuerriegel
45
18
0
13 Sep 2022
Counterfactual Analysis of the Impact of the IMF Program on Child
  Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Sourabh Vivek Balgi
J. Peña
Adel Daoud
27
4
0
17 Feb 2022
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
Chris Cundy
Aditya Grover
Stefano Ermon
CML
40
72
0
06 Dec 2021
Embedded-model flows: Combining the inductive biases of model-free deep
  learning and explicit probabilistic modeling
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Gianluigi Silvestri
Emily Fertig
David A. Moore
L. Ambrogioni
BDL
TPM
AI4CE
25
3
0
12 Oct 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
282
9,136
0
06 Jun 2015
1