Past Sessions:
[Access past sessions (slides + recordings) via following Link using the Password: Causa1ity, Direct Access Link]

• Session 14.02.2024 | Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions | Discussant: Duligur Ibeling
• Session 24.01.2024 | Causal Discovery with Language Models as Imperfect Experts | Discussant: Valentina Zantedeschi
• Session 10.01.2024 | Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding | Discussant: Graham Van Goffrier
• Session 20.12.2023 | Rewind 2023 | Final session of 2023, some statistics, some recap, some wishes for the future!
• Session 06.12.2023 | Causal Discovery for Linear Mixed Data | Discussant: Yan Zeng
• Session 29.11.2023 | Towards efficient representation identification in supervised learning | Discussant: Divyat Mahajan
• Session 22.11.2023 | Causal Inference with Non-IID Data using Linear Graphical Models | Discussant: Chi Zhang
• Session 15.11.2023 | Generative multitask learning mitigates target-causing confounding | Discussant: Taro Makino
• Session 25.10.2023 | Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies | Discussant: Shachi Deshpande (शची देशपांडे)
• Session 18.10.2023 | Foundations of Causal Discovery on Groups of Variables | Discussant: Jonas Wahl
• Session 11.10.2023 | Causal Inference Theory with Information Dependency Models | Discussant: Benjamin Heymann
• Session 27.09.2023 | Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations | Discussant: Michel Besserve
• Session 20.09.2023 | Measuring axiomatic soundness of counterfactual image models | Discussant: Miguel Monteiro
• Session 13.09.2023 | Causal normalizing flows: from theory to practice | Discussant: Adrián Javaloy
• Session 30.08.2023 | Causal-structure Driven Augmentations for Text OOD Generalization | Discussant: Amir Feder
• Session 23.08.2023 | Partial Identification of Dose Responses with Hidden Confounders | Discussant: Myrl Marmarelis
• Session 16.08.2023 | Anticipating Performativity by Predicting from Predictions | Discussant: Celestine Mendler-Dünner
• Session 26.07.2023 | Towards Causal Model-Based Engineering in Automotive System Safety | Discussant: Robert Maier
• Session 19.07.2023 | Making a (Counterfactual) Difference One Rationale at a Time | Discussant: Mitchell Plyler
• Session 12.07.2023 | Attainability and Optimality: The Equalized Odds Fairness Revisited | Discussant: Zeyu Tang (唐泽宇)
• Session 14.06.2023 | Backtracking Counterfactuals | Discussant: Julius von Kügelgen
• Session 07.06.2023 | Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data | Discussant: Sindy Löwe
• 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:

• 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

Are you interested in Graphs and Geometry for Machine Learning?