Causal Inference & Machine Learning: Why now?

December 13th, 2021

WHY-21 @ NeurIPS

Accepted Papers
  • A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
    Xiaoqing Tan, Chung-Chou Ho Chang, Lu Tang
  • Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
    Sindy Löwe, David Madras, Richard Zemel, Max Welling
  • BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
    Chris Cundy, Aditya Grover, Stefano Ermon
  • Building Object-based Causal Programs for Human-like Generalization
    Bonan Zhao, Chris Lucas, Neil Bramley
  • Causal Expectation-Maximisation
    Marco Zaffalon, Alessandro Antonucci*, Rafael Cabañas
  • Causal Inference Using Tractable Circuits
    Adnan Darwiche
  • Conditional average treatment effect estimation with treatment offset models
    Wouter AC van Amsterdam, Rajesh Ranganath
  • Desiderata for Representation Learning: A Causal Perspective
    Yixin Wang, Michael Jordan
  • DiBS: Differentiable Bayesian Structure Learning
    Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause
  • Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
    Olivier Jeunen, Ciaran M Gilligan-Lee, Rishabh Mehrotra, Mounia Lalmas
  • Encoding Causal Macrovariables
    Benedikt Höltgen
  • Identification of Latent Graphs: A Quantum Entropic Approach
    Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
  • Learning Neural Causal Models with Active Interventions
    Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary K
  • Learning preventative and generative causal structures from point events in continuous time
    Tianwei Gong, Neil Bramley
  • MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data
    Johannes Huegle, Christopher Hagedorn, Lukas Böhme, Mats Pörschke, Jonas Umland, Rainer Schlosser
  • Multiple Environments Can Reduce Indeterminacy in VAEs
    Quanhan Xi, Benjamin Bloem-Reddy
  • On the Adversarial Robustness of Causal Algorithmic Recourse
    Ricardo Dominguez-Olmedo, Amir H Karimi, Bernhard Schölkopf
  • Prequential MDL for Causal Structure Learning with Neural Networks
    Jorg Bornschein, Silvia Chiappa, Alan Malek, Nan Rosemary Ke
  • Reliable causal discovery based on mutual information supremum principle for finite datasets
    Vincent Cabeli, Honghao Li, Marcel da Câmara Ribeiro-Dantas, Franck Simon, Herve Isambert
  • Scalable Causal Domain Adaptation
    Mohammad Ali Javidian, Om Pandey, Pooyan Jamshidi
  • Synthesis of Causal Reactive Programs with Structured Latent State
    Ria Das, Joshua Tenenbaum, Armando Solar-Lezama, Zenna Tavares
  • Typing assumptions improve identification in causal discovery
    Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sebastien Lachapelle, Alexandre Drouin
  • Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
    Thien Q Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
  • Using Embeddings to Estimate Peer Influence on Social Networks
    Irina Cristali, Victor Veitch
  • Using Non-Linear Causal Models to StudyAerosol-Cloud Interactions in the Southeast Pacific
    Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier