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