Awesome Causal Inference

(Note that Causal Inference is highly related to Awesome Explainable/Interpretable AI, another similar collection Overview of Causal Inference)









Series: Challenges in Machine Learning


  1. Yoshua Bengio’s IEEE Spectrum interview:

Causality is very important for the next steps of the progress of machine learning.

  1. Yoshua Bengio’s NeurIPS 19 talk

From System 1 Deep Learning to System 2 Deep Learning

  1. Leon Bottou 's keynote at ICLR on causality.

Learning Representation using Causal Invariance

  1. Judea Pearl’s interview at CogSci 2011

“There is no free will, which is a useful illusion”

  1. Judea Pearl, 2012 ACM A.M. Turing Award Lecture

The Mechanization of Causal Inference

  1. Judea Pearl’s talk at AI Podcast

Causal Reasoning, Counterfactuals, Bayesian Networks, and the Path to AGI

  1. IDSS Distinguished Seminar Speaker Susan Athey
  1. Causal Discovery with Reinforcement Learning | OpenReview
  • ICLR 2020 triple 8 rating paper
  • Causal Discovery on static DAG by using RL
  1. [1802.05842] Neural Granger Causality
  • Causality for Time Series data
  • Not a great paper
  • The method is very straightforward.
  • Can be used as a baseline
  1. and Bi-directional Causal Graph Learning through Weight-Sharing and Low-Rank Neural Network | IEEE Conference Publication | IEEE Xplore
  • Causality for Time Series data
  • Seems like duplicated submissions
  • The method is over complicated imo
  1. MAKE | Free Full-Text | Causal Discovery with Attention-Based Convolutional Neural Networks
  • Causality for Time Series data
  • with a lot of background knowledge. Must read
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