Fetching the paper…
Reading the bibliography…
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Inference and missing data
Donald B Rubin · 1976
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
Peter Spirtes and Clark Glymour · 1991
Earlier work this paper cites.
Identification of causal effects using instrumental variables
Joshua D Angrist, Guido W Imbens, and Donald B Rubin · 1996
Earlier work this paper cites.
A bayesian approach to causal discovery
David Heckerman, Christopher Meek, and Gregory Cooper · 1999
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Optimization ii. numerical methods for nonlinear continuous optimization
AS Nemirovsky · 1999
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
David Maxwell Chickering · 2002
Earlier work this paper cites.
The costs of low birth weight
Douglas Almond, Kenneth Y Chay, and David S Lee · 2005
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 linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
Earlier work this paper cites.
Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
Tim Van den Bulcke, Koenraad Van Leemput, Bart Naudts, Piet van Remortel, Hongwu Ma, Alain Verschoren, Bart De Moor, and Kathleen Marchal · 2006
Earlier work this paper cites.
Extensions of ica for causality discovery in the hong kong stock market
Kun Zhang and Lai-Wan Chan · 2006
Earlier work this paper cites.
Estimating high-dimensional directed acyclic graphs with the pc-algorithm
Markus Kalisch and Peter Bühlman · 2007
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
Earlier work this paper cites.
Causal inference in statistics: An overview
Judea Pearl · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
K ZHANG · 2009
Earlier work this paper cites.
Identifying cause and effect on discrete data using additive noise models
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2010
Earlier work this paper cites.
Matching methods for causal inference: A review and a look forward
Elizabeth A Stuart · 2010
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Earlier work this paper cites.
On causal discovery with cyclic additive noise model
Joris M Mooij, Dominik Janzing, Tom Heskes, and Bernhard Schölkopf · 2011
Earlier work this paper cites.
A bayesian approach to constraint based causal inference
Tom Claassen and Tom Heskes · 2012
Earlier work this paper cites.
Missforest—non-parametric missing value imputation for mixed-type data
Daniel J Stekhoven and Peter Bühlmann · 2012
Cited alongside, same era.
On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvarinen · 2012
Cited alongside, same era.
Pairwise likelihood ratios for estimation of non-gaussian structural equation models
Aapo Hyvärinen and Stephen M Smith · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Directed cyclic graphical representations of feedback models
Peter L Spirtes · 2013
Cited alongside, same era.
Fast causal inference with non-random missingness by test-wise deletion
Eric V Strobl, Shyam Visweswaran, and Peter L Spirtes · 2018
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2018
Later among the works it cites.
Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellstrom, and Stephan Mandt · 2018
Later among the works it cites.
Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
Later among the works it cites.
Estimating counterfactual treatment outcomes over time through adversarially balanced representations
Ioana Bica, Ahmed M Alaa, James Jordon, and Mihaela van der Schaar · 2019
Later among the works it cites.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cam: Causal additive models, high-dimensional order search and penalized regression
Peter Bühlmann, Jonas Peters, and Jan Ernest · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh and Peter Bühlmann · 2014
Cited alongside, same era.
Identifiability of gaussian structural equation models with equal error variances
Jonas Peters and Peter Bühlmann · 2014
Cited alongside, same era.
Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
Cited alongside, same era.
David Maxwell Chickering and Christopher Meek · 2015
Cited alongside, same era.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Cited alongside, same era.
Later among the works it cites.
Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
Later among the works it cites.
Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2019
Later among the works it cites.
A graph autoencoder approach to causal structure learning
Ignavier Ng, Shengyu Zhu, Zhitang Chen, and Zhuangyan Fang · 2019
Later among the works it cites.
Causal discovery in the presence of missing data
Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, and Kun Zhang · 2019
Later among the works it cites.
Statistically efficient greedy equivalence search
Max Chickering · 2020
Later among the works it cites.
On the role of sparsity and dag constraints for learning linear dags
Ignavier Ng, AmirEmad Ghassami, and Kun Zhang · 2020
Later among the works it cites.
Learning sparse nonparametric dags
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing · 2020
Later among the works it cites.
Estimating the long-term effects of novel treatments
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, and Vasilis Syrgkanis · 2021
Later among the works it cites.
Minimal enumeration of all possible total effects in a markov equivalence class
Richard Guo and Emilija Perkovic · 2021
Later among the works it cites.
Diagnosis of autism spectrum disorder by causal influence strength learned from resting-state fmri data
Biwei Huang · 2021
Later among the works it cites.
Estimating identifiable causal effects on markov equivalence class through double machine learning
Yonghan Jung, Jin Tian, and Elias Bareinboim · 2021
Later among the works it cites.
Unsuitability of NOTEARS for causal graph discovery
Marcus Kaiser and Maksim Sipos · 2021
Later among the works it cites.
Causal autoregressive flows
Ilyes Khemakhem, Ricardo Monti, Robert Leech, and Aapo Hyvarinen · 2021
Later among the works it cites.
Efficient neural causal discovery without acyclicity constraints
Phillip Lippe, Taco Cohen, and Efstratios Gavves · 2021
Later among the works it cites.
Beware of the simulated dag! varsortability in additive noise models
Alexander G. Reisach, Christof Seiler, and Sebastian Weichwald · 2021
Later among the works it cites.
Dowhy: Addressing challenges in expressing and validating causal assumptions
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, and Emre Kıcıman · 2021
Later among the works it cites.
gcastle: A python toolbox for causal discovery
Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, and Lujia Pan · 2021
Later among the works it cites.
Neural autoregressive flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2087
Closest in time.