Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI

AAAI Spring Symposium, March 25-27, 2019, Stanford, CA

Accepted Papers
Session 1
  When: Mar/25, 9:10am - 10:30am (10 mins each)
  Where: Room 02, History Building (building 200)
  • Approximating Algorithmic Conditional Independence for Discrete Data
    Alexander Marx and Jilles Vreeken
  • Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning
    Wouter van Amsterdam and Rene Eijkemans
  • The Blessings of Multiple Causes: A Causal Graphical View
    Yixin Wang and David Blei
  • A novel approach to handling hard missing data problems
    Karthika Mohan
  • The Causal Interpretations of Bayesian Hypergraphs
    Zhiyu Wang, Mohammad Javidian, Linyuan Lu and Marco Valtorta
  • Inference For The Smoothed Proportion Whose Average Treatment Effect Exceeds a Threshold
    Jonathan Levy and Mark van der Laan
  • Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
    Mohammad Ali Javidian, Pooyan Jamshidi and Marco Valtorta
  • Causal Inference based on Undersmoothing the Highly Adaptive Lasso
    Mark van der Laan, David Benkeser and Wilson Cai
Session 2
  When: Wednesday (Mar/27), 9:00am - 10:30am (10 mins each)
  Where: Room 02, History Building (building 200)
  • Towards a Modal Logic of Causal Counterfactuals Predictions
    Kenneth Lai and James Pustejovsky
  • Counterfactual learning in networks: an empirical study of model dependence
    Usman Shahid and Elena Zheleva
  • Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables
    Amit Sharma
  • Pooling vs Voting: An Empirical Study of Learning Causal Structures
    Meghamala Sinha, Prasad Tadepalli and Stephen Ramsey
  • Learning Causal Trees with Latent Variables via Controlled Experimentation
    Prasad Tadepalli, Cameron Barrie and Stuart Russell
  • Vector-induced spectral measures and instrument exogeneity
    Patrick Burauel
  • Whittemore: An embedded domain specific language for causal programming
    Joshua Brule
  • Work-In-Progress: Ensemble Causal Learning for Modeling Post-Partum Depression
    Nandini Ramanan and Sriraam Natarajan
  • On modelling emergence of logical thinking
    Cristian Ivan and Bipin Indurkhya