Machine Learning has been extremely successful throughout many critical areas, including computer vision, natural language processing, and game-playing. Still, a growing segment of the machine learning community recognizes that there are still fundamental pieces missing from the AI puzzle, among them causal inference.
This recognition comes from the observation that even though causality is a central component found throughout the sciences, engineering, and many other aspects of human cognition, explicit reference to causal relationships is largely missing in current learning systems. This entails a new goal of integrating causal inference and machine learning capabilities into the next generation of intelligent systems, thus paving the way towards higher levels of intelligence and human-centric AI. The synergy goes in both directions; causal inference benefitting from machine learning and the other way around. Current machine learning systems lack the ability to leverage the invariances imprinted by the underlying causal mechanisms towards reasoning about generalizability, explainability, interpretability, and robustness. Current causal inference methods, on the other hand, lack the ability to scale up to high-dimensional settings, where current machine learning systems excel.
The goal of this workshop is to bring together researchers from both camps to initiate principled discussions about the integration of causal reasoning and machine learning perspectives to help tackle the challenging AI tasks of the coming decades. We welcome researchers from all relevant disciplines, including but not limited to computer science, cognitive science, robotics, mathematics, statistics, physics, and philosophy.
| Intro | |
| Uri Shalit - Calibration, out-of-distribution generalization and a path towards causal representations (Invited Talk) | |
| Julius von Kügelgen - Independent mechanism analysis, a new concept? (Invited Talk) | |
| David Blei - On the Assumptions of Synthetic Control Methods (Invited Talk) | |
| Session 1: Q&A (Q&A) | |
| Ricardo Silva - The Road to Causal Programming (Invited Talk) | |
| Aapo Hyvarinen - Causal discovery by generative modelling (Invited Talk) | |
| Tobias Gerstenberg - Going beyond the here and now: Counterfactual simulation in human cognition (Invited Talk) | |
| Session 2: Q&A (Q&A) | |
| Poster Session | |
| Thomas Icard - A (topo)logical perspective on causal inference (Invited Talk) | |
| Caroline Uhler: TBA (Invited Talk) | |
| Rosemary Ke - From "What" to "Why": towards causal learning (Invited Talk) | |
| Session 3: Q&A (Q&A) | |
| Judea Pearl - The logic of Causal Inference (Keynote Speaker) | |
| Discussion Panel | |
| Zaffalon, Antonucci, Cabañas - Causal Expectation-Maximisation (Contributed Talk) | |
| Dominguez Olmedo, Karimi, Schölkopf - On the Adversarial Robustness of Causal Algorithmic Recourse (Contributed Talk) | |
| Javidian, Pandey, Jamshidi - Scalable Causal Domain Adaptation (Contributed Talk) | |
| Cundy, Grover, Ermon - BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (Contributed Talk) | |
| Alison Gopnik - Casual Learning in Children and Computational Models (Invited Talk) | |
| Adèle Ribeiro - Effect Identification in Cluster Causal Diagrams (Invited Talk) | |
| Victor Chernozhukov - Omitted Confounder Bias Bounds for Machine Learned Causal Models (Invited Talk) | |
| Session 4: Q&A (Q&A) | |
| Closing Remarks | |
| DiBS: Differentiable Bayesian Structure Learning (Poster) | |
| Learning Neural Causal Models with Active Interventions (Poster) | |
| Using Embeddings to Estimate Peer Influence on Social Networks (Poster) | |
| Reliable causal discovery based on mutual information supremum principle for finite datasets (Poster) | |
| Scalable Causal Domain Adaptation (Poster) | |
| Learning preventative and generative causal structures from point events in continuous time (Poster) | |
| Building Object-based Causal Programs for Human-like Generalization (Poster) | |
| On the Robustness of Causal Algorithmic Recourse (Poster) | |
| Desiderata for Representation Learning: A Causal Perspective (Poster) | |
| Scalable Variational Approaches for Bayesian Causal Discovery (Poster) | |
| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources (Poster) | |
| Multiple Environments Can Reduce Indeterminacy in VAEs (Poster) | |
| Using Non-Linear Causal Models to StudyAerosol-Cloud Interactions in the Southeast Pacific (Poster) | |
| Synthesis of Reactive Programs with Structured Latent State (Poster) | |
| Causal Inference Using Tractable Circuits (Poster) | |
| Causal Expectation-Maximisation (Poster) | |
| Identification of Latent Graphs: A Quantum Entropic Approach (Poster) | |
| Individual treatment effect estimation in the presence of unobserved confounding based on a fixed relative treatment effect (Poster) | |
| MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data (Poster) | |
| Prequential MDL for Causal Structure Learning with Neural Networks (Poster) | |
| Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation (Poster) | |
| Encoding Causal Macrovariables (Poster) | |
| Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data (Poster) | |
| Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders (Poster) | |
| Typing assumptions improve identification in causal discovery (Poster) | |