🌳 Causal Genealogy
Click Here to get an overview of the network of people involved in causality research, a causal graph of causal people so to speak.🌐 Everything to be found in our Slack Community:
Channel #general
for announcements and 'general' thingsChannel
#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 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
Workshop on neuro Causal and Symbolic AI:
Rewatch the NeurIPS 2022 Full-day Event and Check out all the Accepted PapersAre you interested in Graphs and Geometry for Machine Learning?
If so, then consider joining the Learning on Graphs and Geometry Reading Group.