Fetching the paper…
Reading the bibliography…
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Bayesian graphical models for discrete data
David Madigan, Jeremy York, and Denis Allard · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
A bayesian approach to causal discovery
David Heckerman, Christopher Meek, and Gregory Cooper · 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.
Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
Dan Geiger, David Heckerman, et al · 2002
Earlier work this paper cites.
Being bayesian about network structure. a bayesian approach to structure discovery in bayesian networks
Nir Friedman and Daphne Koller · 2003
Earlier work this paper cites.
Interventions and causal inference
Frederick Eberhardt and Richard Scheines · 2007
Earlier work this paper cites.
Learning causal bayesian network structures from experimental data
Byron Ellis and Wing Hung Wong · 2008
Earlier work this paper cites.
Improving the structure mcmc sampler for bayesian networks by introducing a new edge reversal move
Marco Grzegorczyk and Dirk Husmeier · 2008
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Towards a rigorous assessment of systems biology models: the dream3 challenges
Robert J Prill, Daniel Marbach, Julio Saez-Rodriguez, Peter K Sorger, Leonidas G Alexopoulos, Xiaowei Xue, Neil D Clarke, Gregoire Altan-Bonnet, and Gustavo Stolovitzky · 2010
Cited alongside, same era.
Genenetweaver: in silico benchmark generation and performance profiling of network inference methods
Thomas Schaffter, Daniel Marbach, and Dario Floreano · 2011
Cited alongside, same era.
Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Alain Hauser and Peter Bühlmann · 2012
Cited alongside, same era.
Cam: Causal additive models, high-dimensional order search and penalized regression
Peter Bühlmann, Jonas Peters, Jan Ernest, et al · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Elements of causal inference
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Later among the works it cites.
A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
Joseph Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, and Clark Glymour · 2017
Later among the works it cites.
Minimal i-map mcmc for scalable structure discovery in causal dag models
Raj Agrawal, Caroline Uhler, and Tamara Broderick · 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.
Abcd-strategy: Budgeted experimental design for targeted causal structure discovery
Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, and Caroline Uhler · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Addendum on the scoring of gaussian directed acyclic graphical models
Jack Kuipers, Giusi Moffa, David Heckerman, et al · 2014
Cited alongside, same era.
LiNGAM: Non-Gaussian methods for estimating causal structures
Shohei Shimizu · 2014
Cited alongside, same era.
Structure discovery in bayesian networks by sampling partial orders
Teppo Niinimäki, Pekka Parviainen, and Mikko Koivisto · 2016
Cited alongside, same era.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
Cited alongside, same era.
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
Cited alongside, same era.
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, and David Duvenaud · 2017
Cited alongside, same era.
Partition mcmc for inference on acyclic digraphs
Jack Kuipers and Giusi Moffa · 2017
Cited alongside, same era.
Later among the works it cites.
A meta-transfer objective for learning to disentangle causal mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal · 2019
Later among the works it cites.
Integer discrete flows and lossless compression
Emiel Hoogeboom, Jorn WT Peters, Rianne van den Berg, and Max Welling · 2019
Later among the works it cites.
Learning neural causal models from unknown interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, and Yoshua Bengio · 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.
Discrete flows: Invertible generative models of discrete data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole · 2019
Later among the works it cites.
Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
Later among the works it cites.
Amortized learning of neural causal representations
Nan Rosemary Ke, Jane Wang, Jovana Mitrovic, Martin Szummer, Danilo J Rezende, et al · 2020
Later among the works it cites.