Causality Reading Club

Currently the reading group has been put on hold until further notice.


Date Time Room Article Discussant
June 4 14:00 Zoom A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery by Boeken and Mooij Philip Boeken
June 4 14:00 Zoom A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms by Bengio et. al. Noud
May 27 14:00 Zoom Constraint-Based Causal Discovery In The Presence Of Cycles by Joris Mooij and Tom Claassen Joris
May 14 14:00 Zoom Designing Data Augmentation for Simulating Interventions by Maximilian Ilse, Jakub Tomczak and Patrick Forré, Maximilian
May 7 14:00 Zoom Causal Discovery in the Presence of Missing Data by Tu et. al. Philip
Apr 30 14:00 Zoom Learning stable and predictive structures in kinetic systems Stephan
Apr 23 14:00 Zoom A correspondence principle for simultaneous equation models Tineke
Apr 9 14:00 Zoom Causally Correct Partial Models for Reinforcement Learning Noud
Apr 2 14:00 Zoom Out-of-Distribution Generalization via Risk Extrapolation Patrick
Mar 19 14:00 Zoom Constraint-based Causal Structure Learning with Consistent Separating Sets Philip
Mar 5 14:00 F2.02 Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations Noud
Jan 14 13:00 C3.163 Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems by Ness et al. Tineke


Date Time Room Article Discussant
Dec 17 13:00 C3.163 A Bayesian nonparametric test for conditional independence by Onur Teymur et al. Patrick
Dec 3 13:00 C3.163 Causal Regularization by Dominik Janzing Philip
Nov 26 13:00 C3.163 Adjacency-Faithfulness and Conservative Causal Inference by Joseph Ramsey et al. Alexander
Oct 22 13:00 C3.163 Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation by Ruibo Tu et al. Noud
Sep 24 13:00 C3.163 Approximate Causal Abstraction by Sander Beckers et al. Stephan
Sep 10 13:00 C3.163 Active Causal Discovery by Predicting Counterfactual Outcomes Aron Hammond
Aug 27 13:00 C3.163 Invariant Risk Minimization by Martin Arjovsky et al. Patrick
Aug 13 13:00 C3.163 Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach by Tikka et al. Philip
July 16 13:00 C3.163 Density estimation using Real NVP by Laurent Dinh et al. Stephan
July 2 13:00 C3.163 Abstracting Causal Models by Sander Beckers and  Joseph Y. Halpern Tineke
June 18 13:00 C3.163 Causal Confusion in Imitation Learning by De Haan et al. Noud
June 11 13:00 C3.163 Orthogonal Structure Search for Efficient Causal Discovery from Observational Data by Raj et al. Phillip
May 28 13:00 C3.163 Cancelled Cancelled
May 14 13:00 C3.163 Learning Disentangled Representations with Semi-Supervised Deep Generative Models by Siddharth et al. Stephan
Apr 30 13:00 C3.163 Structural Causal Bandits: Where to Intervene? by Lee and Bareinboim Noud
Apr 16 13:00 C3.163 Defining Network Topologies that Can Achieve Biochemical Adaptation by Ma et al. and Perfect and Near-Perfect Adaptation in Cell Signaling by Ferrell Tineke
Apr 2 13:00 C3.163 Cancelled Cancelled
Mar 19 26 13:00 C3.163 Dynamic Chain Graph Models for Ordinal Time Series Data by Behrouzi et al. Pariya Behrouzi
Mar 5 13:00 C3.163 Some of his own work Thijs
Feb 26 13:00 C3.163 Cancelled Cancelled
Feb 19 13:00 C3.163 Canceled Cancelled
Feb 12 13:00 C3.163 Causal Reasoning from Meta-reinforcement Learning by Dasgupta et al. Noud
Feb 5 13:00 A1.14. Small workshop with presentations (mostly) on counterfactuals by Robert van Rooij, Katrin Schultz, and Joris Mooij Joris
Jan 29 13:00 C2.109 Cause-Effect Deep Information Bottleneck For Incomplete Covariates by Parbhoo et al. (2018) Stephan
Jan 15 13:00 C3.163 Equality of Opportunity in Classification: A Causal Approach by Junzhe Zhang and Elias Bareinboim (2018) Tineke


Date Time Room Article Discussant
Dec 18 13:00 C2.109 Learning Predictive Models That Transport by Subbaswamy et al. (2018) Thijs
Dec 11 13:00 C2.109 Cancelled
Dec 4 13:00 C3.163 Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search by Buesing et al. (2018) Noud
Nov 27 13:00 C2.109 Multi-domain Causal Structure Learning in Linear Systems by Ghassami et al. (2018) Philip
Nov 20 13:00 C3.163 Multiple Causal Inference with Latent Confounding by Ranganath and Perotte (2018) Stephan
Nov 13 13:00 C3.163 A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias by Strobl (2018) Patrick
Nov 6 14:00 C3.163 TBD Tineke
Oct 30 14:00 C3.163 TBD Joris
Oct 23 14:00 C3.163 Model selection and local geometry by Evans (2018) Thijs
Oct 16 14:00 C3.163 Learning Functional Causal Models with Generative Neural Networks by Goudet et al. (2017) Philip
Oct 9 14:00 C3.163 Causal Learning for Partially Observed Stochastic Dynamical Systems by Mogensen et al. (2018) Stephan
Oct 2 14:00 C3.163 The inflation technique solves completely the classical inference problem by Navascues and Wolf (2017) Patrick
Sep 25 14:00 C2.109 The Inflation Technique for Causal Inference with Latent Variables by Wolf et al. (2018) [part 2] Tineke
Sep 18 14:00 C3.146 The Inflation Technique for Causal Inference with Latent Variables by Wolf et al. (2018) [part 1] Tineke
Sep 11 14:00 C3.163 The Inferelator by Bonneau et al. (2016) Joris
Jul 12 15:00 C3.146 Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (2015) by Swaminathan and Joachims Philip
Jun 28 15:00 C3.146 Causality and model abstraction by Iwasaki and Simon (1994) [part 2] Stephan
Jun 14 15:00 C3.146 The blessing of multiple causes by Wang and Blei (2018) Patrick
Jun 7 15:00 C3.146 Paper Draft Thijs
May 24 15:00 C3.146 The Blessings of Multiple Causes by Wang and Blei (2018 Patrick
May 17 15:00 C3.146 Information Processing Features Can Detect Behavioral Regimes of Dynamical Systems by Quax et al. (2017) Rick Quax
Apr 26 15:00 C3.146 Causality and model abstraction by Iwasaki and Simon (1994) [part 1] Tineke
Apr 12 15:00 C3.146 Joint Causal Inference from Multiple Datasets by et al. (2018) Joris
Apr 5 15:00 C3.146 Efficient Structure Learning of Bayesian Networks using Constraints by de Campos and Ji (2011) Thijs
Mar 29 15:00 C3.146 Consistency Guarantees for Permutation-Based Causal Inference Algorithms by Solus et al. (2017)  Philip
Mar 2 15:00 C3.146 On the latent space of Wasserstein Auto-Encoders by Rubinstein et al . (2018) Stephan
Mar 1 15:00 C3.146 Draft reviews All
Feb 15 15:00 C3.146 Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling by Burkart and Király (2017) Patrick
Jan 11 15:00 C3.146 Extended Conditional Independence and Applications in Causal Inference by Constantinou and Dawid (2017) Patrick


Date Time Room Article Discussant
Dec 21 15:00 C3.146 Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry by Lun et al.(2017) Tineke Blom
Nov 16 15:00 C3.146 Causal inference using the algorithmic Markov condition by Janzing and Schoelkopf (2008) and Causal Markov condition for submodular information measures by Steudel et al. (2010) Patrick Forré
Nov 9 15:00 C3.146 Telling Cause from Effect using MDL-based Local and Global Regression by Marx and Vreeken (2017) Thijs van Ommen
Nov 2 15:00 C3.146 Implicit Causal Models for Genome-wide Association Studies by Tran and Blei (2017) Joris Mooij
Oct 28 15:00 C3.146 Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges by Eigenmann et al. (2017) Tineke Blom
Oct 12 15:00 C3.146 Identifying Best Interventions through Online Importance Sampling by Sen et al. (2017) Philip Versteeg
Sep 28 15:00 C3.146 Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information by Jakob Runge (2017) Patrick Forré
Sep 21 15:00 C3.146 Avoiding Discrimination through Causal Reasoning by Kilbertus et al.  (2017) Sara Magliacane
Sep 14 15:00 C3.146 CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training by Kocaoglu et al. (2017) Patrick Forré
Aug 25 14:00 C3.146 Paper draft Patrick Forré
Aug 18 14:00 C3.146 Ch5-8 of Counterfactual Reasoning and Learning Systems by Bottou et al. (2013) Philip Versteeg
Aug 11 14:00 C3.146 Ch1-4 of Counterfactual Reasoning and Learning Systems by Bottou et al. (2013) Philip Versteeg
Jul 28 14:00 C3.146 Discovering Causal Signals in Images by Lopez-Paz et al. (2017) Patrick Forré
Jul 21 14:00 C3.146 Margins of discrete Bayesian networks by Evans (preprint) Thijs van Ommen
Jul 14 14:00 C3.146 Revisiting Classifier Two-Sample Tests by D. Lopez-Paz and M. Oquab (2016) Stephan Bongers
Jul 7 14:00 C3.146 Causal Discovery in the Presence of Measurement Error: Identifiability Conditions by Zhang (2017) Tineke Blom
Jun 16 14:00 C3.146 Zhang et al. (2013), Zhang et al. (2015) and Gong et al. (2016) Tineke, Stephan and Thijs
May 12 14:00 C3.146 On Causal and Anticausal Learning by Scholkopf et al (2012) Sara Magliacane
Mar 17 14:00 C3.146 Paper draft Stephan Bongers
Mar 10 14:00 C3.146 Strong completeness and faithfulness in Bayesian networks by Meek (1995) Joris Mooij
Mar 3 14:00 C3.146 Unifying Markov Properties for Graphical Models by Lauritzen and Sadeghi (preprint) Patrick Forré
Feb 24 14:00 C3.146 Causal Bandits: Learning Good Interventions via Causal Inference by Lattimore, Lattimore and Reid (2016) Stephan Bongers
Feb 17 14:00 C3.146 Bandits with Unobserved Confounders: A Causal Approach by Bareinboim, Forney and Pearl (2016) Philip Versteeg
Feb 10 14:00 C3.146 Joint Causal Inference (2016) by Magliacane, Claassen and Mooij Sara Magliacane
Feb 3 14:00 C3.146 Paper draft Christos Louizos
Jan 27 14:00 C3.146 Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models (2006) by Shpitser and Pearl Patrick Forré
Jan 13 14:00 C3.146 Causal inference and the data-fusion problem by Bareinboim and Pearl (2016) Philip Versteeg


Date Time Room Article Discussant
Jan 08 14:30 C3.146 Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing by Benjamini and Hochberg Philip Versteeg
Feb 05 14:00 C3.146 Causation Prediction and Search (chapters 1&2) by Spirtes and Glymour and Scheines Joris Mooij
Feb 12 14:00 C3.146 Causation Prediction and Search (chapter 3) by Spirtes and Glymour and Scheines Alexander Ly
Feb 19 14:00 C3.146 Causation Prediction and Search (3.5 – 3.9) by Spirtes and Glymour and Scheines Alexander Ly
Feb 26 14:00 C3.146 Causation Prediction and Search (chapter 4) by Spirtes and Glymour and Scheines Thijs van Ommen
Mar 4 14:00 C3.146 Causation Prediction and Search (chapter 5.1-5.4) by Spirtes and Glymour and Scheines Stephan Bongers
Mar 11 14:00 C3.146 Causation Prediction and Search (chapter 5.5-5.10) by Spirtes and Glymour and Scheines Philip Versteeg
Mar 18 14:00 C3.146 Causation Prediction and Search (chapter 6) by Spirtes and Glymour and Scheines Sara Magliacane
Apr 15 14:00 C3.146 Causation Prediction and Search (chapter 7) by Spirtes and Glymour and Scheinces Joris Mooij
Apr 22 14:00 C3.146 Inferring the Causal Direction Privately by Kusner and Sun and Sridharan and Weinberger Mijung Park
Apr 29 14:00 C3.146 On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias by Zhang (2008) Joris Mooij
May 13 14:00 C3.146 Causal inference using invariant prediction: identification and confidence intervals by Peters and Bühlmann and Meinshausen (2016) Alexander Ly
May 20 14:00 C3.146 Quantifying Causal Influences (2012) by Janzing and Balduzzi and Grosse-Wentrup and Schölkopf Rick Quax
May 27 14:00 C3.146 Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models by Frey (2003) and Causality with Gates (2012) by Winn Sara Magliacane
Jul 1 14:00 C3.146 Stephan's draft on Markov properties of graphical representations of acyclic structural causal models Stephan Bongers
Jul 8 14:00 C3.146 The central role of the propensity score in observational studies for causal effects (1983) by Rosenbaum and Rubin Thomas Klaus
Jul 22 14:00 C3.146 Learning Optimal Interventions by Mueller and Reshef and Du and Jaakkola Mijung Park
Jul 29 14:00 C3.146 ICML 2016 Tutorial Causal Inference for Observational Studies by David Sontag and Uri Shalit Joris Mooij
Aug 05 14:00 C3.146 Causality Video Club
Aug 12 14:00 C3.146 Half-trek criterion for generic identifiability of linear structural equation models by Foygel and Draisma and Drton Thijs van Ommen
Aug 19 14:00 C3.146 Graphs for Margins of Bayesian Networks (2016) by Robin Evans Patrick Forré
August 26 14:00 C3.146 Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values (2016) by Strobl and Spirtes and Visweswaran Sara Magliacane
Sep 02 14:00 C3.146 Causality Video Club
Sep 09 14:00 C3.146 The Logic of Structure-Based Counterfactuals [sections 7.1-7.3 in Causality: Models Reasoning and Inference (2009)] by Judea Pearl Joris Mooij
Sep 16 14:00 C3.146 Some Title by Peters Janzing and Schölkopf (2016) [ch. 1] Stephan Bongers
Sep 23 14:00 C3.146 Some Title by Peters Janzing and Schölkopf (2016) [chs. 2-3] Stephan Bongers
TBA 14:00 C3.146 Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization (2015) by Swaminathan and Joachims Thorsten Joachims
Nov 4 14:00 C3.146 Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 1-3] Joris Mooij
Nov 11 14:00 C3.146 Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 4-6]  Tineke Blom
Nov 18 14:00 C3.146 Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 6–7] Tineke Blom
Nov 25 14:00 C3.146 Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 8-10]  Thijs van Ommen
Dec  16 14:00 C3.146 Identifying independence in Bayesian Networks by Geiger Verma and Pearl (1990)   Tom Claassen

Older schedules can be found here.