Fetching the paper…
Reading the bibliography…
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.
Evaluating the econometric evaluations of training programs with experimental data
Robert J LaLonde · 1986
Earlier work this paper cites.
Inversion theory and conformal mapping , volume 9
David E Blair · 2000
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A. Lauffenburger, and Garry P. Nolan · 2005
Earlier work this paper cites.
A kernel statistical test of independence
Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song, Bernhard Schölkopf, and Alex Smola · 2007
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvarinen · 2012
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Causal Inference in Statistics: A Primer
J. Pearl, M. Glymour, and N.P. Jewell · 2016
Earlier work this paper cites.
Bayesian inference of individualized treatment effects using multi-task gaussian processes
Ahmed M Alaa and Mihaela Van Der Schaar · 2017
Earlier work this paper cites.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2017
Earlier work this paper cites.
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
Earlier work this paper cites.
Causalgan: Learning causal implicit generative models with adversarial training
Murat Kocaoglu, Christopher Snyder, Alexandros G Dimakis, and Sriram Vishwanath · 2018
Earlier work this paper cites.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Cited alongside, same era.
DoWhy: A Python package for causal inference
Amit Sharma, Emre Kiciman, et al · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius Von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2020
Cited alongside, same era.
Sample-efficient reinforcement learning via counterfactual-based data augmentation
Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, and Bernhard Schölkopf · 2020
Cited alongside, same era.
Can: A causal adversarial network for learning observational and interventional distributions
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Personalized public policy analysis in social sciences using causal-graphical normalizing flows
Sourabh Balgi, Jose M Pena, and Adel Daoud · 2022
Later among the works it cites.
Dowhy-gcm: An extension of dowhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, and Dominik Janzing · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Later among the works it cites.
On noise abduction for answering counterfactual queries: A practical outlook
Saptarshi Saha and Utpal Garain · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Raha Moraffah, Bahman Moraffah, Mansooreh Karami, Adrienne Raglin, and Huan Liu · 2020
Cited alongside, same era.
Causal inference with deep causal graphs
Álvaro Parafita and Jordi Vitrià · 2020
Cited alongside, same era.
Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Estimating causal effects with the neural autoregressive density estimator
Sergio Garrido, Stanislav Borysov, Jeppe Rich, and Francisco Pereira · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Causal autoregressive flows
Ilyes Khemakhem, Ricardo Monti, Robert Leech, and Aapo Hyvarinen · 2021
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding, 2022
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
Diffusion causal models for counterfactual estimation
Pedro Sanchez and Sotirios A. Tsaftaris · 2022
Later among the works it cites.
Diffusion models for causal discovery via topological ordering
Pedro Sanchez, Xiao Liu, Alison Q O’Neil, and Sotirios A Tsaftaris · 2022
Later among the works it cites.
Vaca: Designing variational graph autoencoders for causal queries
Pablo Sánchez-Martin, Miriam Rateike, and Isabel Valera · 2022
Later among the works it cites.
Causal normalizing flows: from theory to practice, 2023
Adrián Javaloy, Pablo Sánchez-Martín, and Isabel Valera · 2023
Closest in time.
Counterfactual (non-) identifiability of learned structural causal models
Arash Nasr-Esfahany and Emre Kiciman · 2023
Closest in time.
Counterfactual identifiability of bijective causal models
Arash Nasr-Esfahany, Mohammad Alizadeh, and Devavrat Shah · 2023
Closest in time.
Identifying patient-specific root causes with the heteroscedastic noise model
Eric V Strobl and Thomas A Lasko · 2023
Closest in time.
A data resource from concurrent intracranial stimulation and functional MRI of the human brain
W. H. Thompson, R. Nair, H. Oya, O. Esteban, J. M. Shine, C. I. Petkov, R. A. Poldrack, M. Howard, and R. Adolphs · 2052
Closest in time.