Causality Reading Club

The Causality Reading group is weekly event organized internally by the Causality group led by prof. Joris Mooij. Due to the global pandemic, this is held online over Zoom. Anyone interested in the field is free to participate, you can get information on how to join by sending an email to aawmdekroon (at) gmail.com.

2020

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

2019

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

2018

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

2017

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

 2016

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.