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

• 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:

• Code Tutorial by Alexandre Drouin on various topics featuring Simpson's paradox, identification through adjustment and estimation using machine learning
• Code Tutorial by Matej Zečević on various topics featuring why we actually need causality, the Pearlian causal hierarchy and bounding causal effects
• “The Book of Why” (2018) by Judea Pearl & Dana Mackenzie for an intuitive, general audience introduction into Pearlian causality
• “Causality” Book (CUP, 2009) by Judea Pearl as the original, rigorous treatise on Pearlian causality
• “Elements of Causal Inference” (MITP, 2017) by Jonas Peters et al. providing different and additional perspective on Pearlian causality especially w.r.t. machine learning
• Lecture Series “Causality” by Jonas Peters (2017) covering key topics from the previously listed literature (focus on Peters et al.)
• Online Course ”Introduction to Causal Inference” by Brady Neal (2020) covering key topics from the previously listed literature (focus on Pearl)
• Lecture “Causal Data Science” by Elias Bareinboim (2019) on several advanced topics as well as future perspectives on the field
• “Causal Inference in Statistics” by Judea Pearl et al. (2016) compressed view on Pearlian causality for a statistics educated audience
• More Online Meetings on Causal Inference (Groups/Seminars with different Systems): CIIG, OCIS

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