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Join the Discussion!

We get together with the authors of papers in Causality x AI/ML to discuss their papers in a lively group discussion. We meet weekly on Wednesday at 15:30 UTC / 16:30 CEST/CET in summer/winter / 10:30 EST / 23:30 JST.

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"If the discussions become too causal, then we might as well make it more casual." (warning: a joke that requires looking closely)

🌐 Everything to be found in our Slack Community:
Channel
#general
for announcements and 'general' things
Channel
#vote
for determining the papers of our future sessions (includes large list of candidate papers)
Channel
#recordings
for rewatching past sessions (includes slides)

Past Sessions: [Password: Causa1ity, Direct Access Link]

  • Session 31.05.2023 | Amortized Inference for Causal Structure Learning | Discussant: Lars Lorch
  • Session 24.05.2023 | Typing Assumptions Improve Identification in Causal Discovery | Discussant: Philippe Brouillard
  • Session 17.05.2023 | Compositional Probabilistic and Causal Inference using Tractable Circuit Models | Discussant: Benjie Wang
  • Session 10.05.2023 | Jacobian-based Causal Discovery with Nonlinear ICA | Discussant: Patrik Reizinger
  • Session 03.05.2023 | A New Constructive Criterion for Markov Equivalence of MAGs | Discussant: Marcel Wienöbst
  • Session 26.04.2023 | Can Humans Be out of the Loop? | Discussant: Junzhe Zhang
  • Session 19.04.2023 | Diffusion Visual Counterfactual Explanations | Discussant: Valentyn Boreiko
  • Session 12.04.2023 | Regret Minimization for Causal Inference on Large Treatment Space | Discussant: Akira Tanimoto (谷本啓)
  • Session 05.04.2023 | Using Embeddings for Causal Estimation of Peer Influence in Social Networks | Discussant: Irina Cristali
  • Session 29.03.2023 | GRAPL: A computational library for nonparametric SCM, analysis and inference | Discussant: Max Little
  • Session 22.03.2023 | Differentiable Causal Discovery Under Latent Interventions | Discussant: Gonçalo Rui Alves Faria
  • Session 15.03.2023 | Diffusion Causal Models for Counterfactual Estimation | Discussant: Pedro Sanchez
  • Session 08.03.2023 | Exploring the Latent Space of Autoencoders with Interventional Assays | Discussant: Felix Leeb
  • Session 01.03.2023 | Deep Counterfactual Estimation with Categorical Background Variables | Discussant: Edward De Brouwer
  • Session 22.02.2023 | Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments | Discussant: Osman Ali Mian
  • Session 08.02.2023 | CLEAR: Generative Counterfactual Explanations on Graphs | Discussants: Jing Ma, Ruocheng Guo
  • Session 01.02.2023 | Causal Transformer for Estimating Counterfactual Outcomes | Discussant: Valentyn Melnychuk
  • Session 25.01.2023 | Abstracting Causal Models | Discussant: Sander Beckers
  • Session 18.01.2023 | Desiderata for Representation Learning: A Causal Perspective | Discussant: Yixin Wang
  • Session 11.01.2023 | Causal Feature Selection via Orthogonal Search | Discussant: Ashkan Soleymani
  • Session 14.11.2022 | Rewind 2022 | Final session of 2022 to simply rewind on what we experienced throughout the year
  • Session 07.12.2022 | Causal Inference Through the Structural Causal Marginal Problem | Discussant: Luigi Gresele
  • Session 30.11.2022 | Selecting Data Augmentation for Simulating Interventions | Discussant: Maximilian Ilse
  • Session 23.11.2022 | On Disentangled Representations Learned from Correlated Data | Discussant: Frederik Träuble
  • Session 16.11.2022 | Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Repr. Learning | Discussant: Sumedh Sontakke
  • Session 09.11.2022 | Causal Machine Learning: A Survey and Open Problems | Discussants: Jean Kaddour, Aengus Lynch
  • Session 02.11.2022 | A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models | Discussant: Severi Rissanen
  • Session 26.10.2022 | Nonlinear Invariant Risk Minimization: A Causal Approach | Discussant: Chaochao Lu
  • Session 19.10.2022 | CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models | Discussant: Mengyue Yang
  • Session 12.10.2022 | Weakly Supervised Causal Representation Learning | Discussant: Johann Brehmer
  • Session 05.10.2022 | Towards Causal Representation Learning | Discussant: Anirudh Goyal
  • Session 21.09.2022 | Selection Collider Bias in Large Language Models | Discussant: Emily McMilin
  • Session 14.09.2022 | The Causal-Neural Connection: Expressiveness, Learnability, and Inference | Discussants: Kai-Zhan Lee, Kevin Xia
  • Session 07.09.2022 | Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style | Discussant: Julius von Kügelgen
  • Session 31.08.2022 | Interventions, Where and How? Experimental Design for Causal Models at Scale | Discussants: Panagiotis Tigas and Yashas Annadani
  • Session 24.08.2022 | Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game | Discussant: Alexander Reisach
  • Session 17.08.2022 | Effect Identification in Cluster Causal Diagrams | Discussants: Tara Anand and Adèle Ribeiro
  • Session 10.08.2022 | Can Foundation Models Talk Causality? | Discussant: Moritz Willig
  • Session 03.08.2022 | Causal Conceptions of Fairness and their Consequences | Discussants: Hamed Nilforoshan and Johann Gaebler
  • Session 28.07.2022 | Bayesian Causal Discovery under Unknown Interventions | Discussant: Alexander Hägele
  • Session 20.07.2022 | Counterfactual Fairness | Discussant: Toon Vanderschueren

Useful Resources:

Are you interested in Graphs and Geometry for Machine Learning?

If so, then consider joining the Learning on Graphs and Geometry Reading Group.