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Understanding causal relationships among the variables of a system is paramount to explain and control its behavior.
The logic of scientific discovery
Popper, K. R. 2005 · 1934
Earlier work this paper cites.
On Random Graphs I
Erdős, P.; and Rényi, A. 1959 · 1959
Earlier work this paper cites.
Graphoids: Graph-Based Logic for Reasoning about Relevance Relations or When Would x Tell You More about y If You Already Know z?
Pearl, J.; and Paz, A. 1986 · 1986
Earlier work this paper cites.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Pearl, J. 1988 · 1988
Earlier work this paper cites.
Identifying Independence in Bayesian Networks
Geiger, D.; Verma, T.; and Pearl, J. 1990 · 1990
Earlier work this paper cites.
Independence Properties of Directed Markov Fields
Lauritzen, S. L.; Dawid, A. P.; Larsen, B. N.; and Leimer, H.-G. 1990 · 1990
Earlier work this paper cites.
Combining Instance-Based and Model-Based Learning
Quinlan, J. R. 1993 · 1993
Earlier work this paper cites.
Bayesian Graphical Models for Discrete Data
Madigan, D.; York, J.; and Allard, D. 1995 · 1995
Earlier work this paper cites.
Strong Completeness and Faithfulness in Bayesian Networks
Meek, C. 1995 · 1995
Earlier work this paper cites.
Learning Bayesian Networks Is NP-Complete
Chickering, D. M. 1996 · 1996
Earlier work this paper cites.
Causation, Prediction, and Search
Spirtes, P.; Glymour, C.; and Scheines, R. 2001 · 2001
Earlier work this paper cites.
Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs
Acid, S.; and de Campos, L. M. 2003 · 2003
Earlier work this paper cites.
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Friedman, N.; and Koller, D. 2003 · 2003
Earlier work this paper cites.
Improving Markov Chain Monte Carlo Model Search for Data Mining
Giudici, P.; and Castelo, R. 2003 · 2003
Earlier work this paper cites.
Large-Sample Learning of Bayesian Networks Is NP-Hard
Chickering, D. M.; Heckerman, D.; and Meek, C. 2004 · 2004
Earlier work this paper cites.
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K.; Perez, O.; Pe’er, D.; Lauffenburger, D. A.; and Nolan, G. P. 2005 · 2005
Earlier work this paper cites.
The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm
Tsamardinos, I.; Brown, L. E.; and Aliferis, C. F. 2006 · 2006
Earlier work this paper cites.
Graphs in Molecular Biology
Huber, W.; Carey, V. J.; Long, L.; Falcon, S.; and Gentleman, R. 2007 · 2007
Earlier work this paper cites.
Reducing Bias through Directed Acyclic Graphs
Shrier, I.; and Platt, R. W. 2008 · 2008
Cited alongside, same era.
Causality: Models, Reasoning and Inference
Pearl, J. 2009 · 2009
Cited alongside, same era.
Action and the Orbit–Stabiliser Theorem
Rose, H. E. 2009 · 2009
Cited alongside, same era.
Kernel-Based Conditional Independence Test and Application in Causal Discovery
Zhang, K.; Peters, J.; Janzing, D.; and Schölkopf, B. 2011 · 2011
Cited alongside, same era.
Conservative Independence-Based Causal Structure Learning in Absence of Adjacency Faithfulness
Lemeire, J.; Meganck, S.; Cartella, F.; and Liu, T. 2012 · 2012
Cited alongside, same era.
Learning Sparse Causal Models Is Not NP-hard
Claassen, T.; Mooij, J. M.; and Heskes, T. 2013 · 2013
Cited alongside, same era.
Visual Causality Analysis Made Practical
Wang, J.; and Mueller, K. 2017 · 2017
Later among the works it cites.
Using Genetic Data to Strengthen Causal Inference in Observational Research
Pingault, J.-B.; O’Reilly, P. F.; Schoeler, T.; Ploubidis, G. B.; Rijsdijk, F.; and Dudbridge, F. 2018 · 2018
Later among the works it cites.
FASK with Interventional Knowledge Recovers Edges from the Sachs Model
Ramsey, J.; and Andrews, B. 2018 · 2018
Later among the works it cites.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Zheng, X.; Aragam, B.; Ravikumar, P. K.; and Xing, E. P. 2018 · 2018
Later among the works it cites.
Scientists rise up against statistical significance
Amrhein, V.; Greenland, S.; and McShane, B. 2019 · 2019
Later among the works it cites.
Review of Causal Discovery Methods Based on Graphical Models
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A Proposed Validation Framework for Expert Elicited Bayesian Networks
Pitchforth, J.; and Mengersen, K. 2013 · 2013
Cited alongside, same era.
Geometry of the Faithfulness Assumption in Causal Inference
Uhler, C.; Raskutti, G.; Bühlmann, P.; and Yu, B. 2013 · 2013
Cited alongside, same era.
Directed Acyclic Graph Kernels for Action Recognition
Wang, L.; and Sahbi, H. 2013 · 2013
Cited alongside, same era.
CAM: Causal Additive Models, High-Dimensional Order Search and Penalized Regression
Bühlmann, P.; Peters, J.; and Ernest, J. 2014 · 2014
Cited alongside, same era.
Identifiability of Gaussian Structural Equation Models with Equal Error Variances
Peters, J.; and Bühlmann, P. 2014 · 2014
Cited alongside, same era.
Visual Causal Feature Learning
Chalupka, K.; Perona, P.; and Eberhardt, F. 2015 · 2015
Cited alongside, same era.
Glymour, C.; Zhang, K.; and Spirtes, P. 2019 · 2019
Later among the works it cites.
Selecting causal brain features with a single conditional independence test per feature
Mastakouri, A.; Schölkopf, B.; and Janzing, D. 2019 · 2019
Later among the works it cites.
Gradient-Based Neural DAG Learning
Lachapelle, S.; Brouillard, P.; Deleu, T.; and Lacoste-Julien, S. 2020 · 2020
Later among the works it cites.
Lecture Notes: Lectures on Graphical Models , volume 3
Lauritzen, S. 2020 · 2020
Later among the works it cites.
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
Shah, R. D.; and Peters, J. 2020 · 2020
Later among the works it cites.
Visual Commonsense Representation Learning via Causal Inference
Wang, T.; Huang, J.; Zhang, H.; and Sun, Q. 2020 · 2020
Later among the works it cites.
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Teshima, T.; and Sugiyama, M. 2021 · 2021
Later among the works it cites.
Causal Structure-Based Root Cause Analysis of Outliers
Budhathoki, K.; Minorics, L.; Bloebaum, P.; and Janzing, D. 2022 · 2022
Later among the works it cites.
Validating Causal Diagrams of Human Health Risks for Spaceflight: An Example Using Bone Data from Rodents
Reynolds, R. J.; Scott, R. T.; Turner, R. T.; Iwaniec, U. T.; Bouxsein, M. L.; Sanders, L. M.; and Antonsen, E. L. 2022 · 2022
Later among the works it cites.
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Rolland, P.; Cevher, V.; Kleindessner, M.; Russell, C.; Janzing, D.; Schölkopf, B.; and Locatello, F. 2022 · 2022
Later among the works it cites.
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