Awesome Causal Inference

[rakuten]

(Note that Causal Inference is highly related to Awesome XAI, another similar collection Overview of Causal Inference)

Tutorial

• KDD 21 tutorial
• Course Introduction to Causal Inference
• ICML 19: Causal Inference and Stable Learning
• KDD 18: Causal Inference and Counterfactual Reasoning
• ICME 19: causal regularized machine learing
• PAKDD 19: causal regularized machine learing
• NIPS 13: Causes and Conterfactuals: Concepts, princeples and tools
• Douly Robustness Estimation
• Live (Video, Slides)

Survey

• A survey on causal inference
• Causality for Machine Learning at ICLR, ACML
• A Survey of Learning Causality with Data: Problems and Methods
• Models of Causal Inference: Going Beyond the Neyman-Rubin-Holland Theory
  
• Causal inference in statistics: An overview (by Judea Pearl)
• An Introduction to Causal Inference (by Judea Pearl)
• Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
• Inferring causality in time series data

Course

Introduction to Causal Inference

Causal Diagrams: Draw Your Assumptions Before Your Conclusions

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Causal Inference

Causal Inference 2

Lectures on Causality: Jonas Peters, Part 1

Collection

Home

https://causalai.github.io/clubjizhi

GitHub - napsternxg/awesome-causality: Resources related to causality

GitHub - rguo12/awesome-causality-algorithms: An index of algorithms for...

GitHub - rguo12/awesome-causality-data: A data index for learning causality.

Researcher

• Judea Pearl
• Leon Bottou
• Peter Bühlmann
• Bernhard Schölkopf
• David Blei
• Aidong Zhang
• Kun Kuang
• Peng Cui

• Fredrik D. Johansson

Papers

• CAUSAL RELATIONAL LEARNING
• Nature Machine Intelligence: Causal deconvolution by algorithmic generative models
• David Blei: The Blessings of Multiple Causes
• The Blessings of Multiple Causes
• Using Embeddings to Correct for Unobserved Confounding in Networks
• ICLR 20: A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
• AAAI 19: Stable Prediction with Model Misspecification and Agnostic Distribution Shift
• ICML 15: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
• ICML 18: Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
• U.S. Department of Energy OSTI: Scalable Causal Graph Learning through a Deep Neural Network
• AAAI 17: Treatment Effect Estimation with Data-Driven Variable Decomposition
• IJCAI 19: On the Estimation of Treatment Effect with Text Covariates
• Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference

• Deep Learning

  • NIPS 19: Adapting Neural Networks for the Estimation of Treatment Effects
  • Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
  • ICML 17: Estimating individual treatment effect: generalization bounds and algorithms
  • ICML 17: Deep Counterfactual Networks with Propensity-Dropout
  • ICML 16: Learning Representations for Counterfactual Inference
  • ICML 17: Deep IV: A Flexible Approach for Counterfactual Prediction
  • NIPS 18: Representation Learning for Treatment Effect Estimation from Observational Data
  • ICLR 20: Learning Disentangled Representations for CounterFactual Regression
  • NIPS 17: Causal Effect Inference with Deep Latent-Variable Models
  • ICLR 18: GANITE: ESTIMATION OF INDIVIDUALIZED TREAT- MENT EFFECTS USING GENERATIVE ADVERSARIAL NETS
  • ICLR 18: CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
  • NIPS 19: Causal Regularization

• Graph

  • WSDM 20: Learning Individual Causal Effects from Networked Observational Data
  • ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
  • NIPS 19: Using Embeddings to Correct for Unobserved Confounding in Networks
  • Vaccines, Contagion, and Social Networks
  • Causal inference for social network data
  • Causal inference, social networks, and chain graphs
  • Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations

• Time Series

  • IJCAI 18: Causal Inference in Time Series via Supervised Learning
  • Detecting causality from short time-series data based on prediction of topologically equivalent attractors
  • NIPS 13: Causal Inference on Time Series using Restricted Structural Equation Models
  • Causality and graphical models in time series analysis
  • Neural Granger Causality for Nonlinear Time Series

• unclassified

  • Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data
  • Who Make Drivers Stop?
    Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
    

Journal

Journal of Causal Inference

Book

https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X/ref=pd_sbs_14_1/135-7508758-4013850?_encoding=UTF8&pd_rd_i=046509760X&pd_rd_r=603f8754-b804-43f3-a8d8-aa910e202c06&pd_rd_w=wGjOx&pd_rd_wg=Ovuzc&pf_rd_p=5873ae95-9063-4a23-9b7e-eafa738c2269&pf_rd_r=F5PD703JJSGWWKHJJP80&psc=1&refRID=F5PD703JJSGWWKHJJP80
https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X/ref=pd_bxgy_img_3/135-7508758-4013850?_encoding=UTF8&pd_rd_i=052189560X&pd_rd_r=62bdbdb6-a851-436e-8183-5a6e23a74daf&pd_rd_w=qBsmP&pd_rd_wg=OS18v&pf_rd_p=09627863-9889-4290-b90a-5e9f86682449&pf_rd_r=0XJTZ9WNA1RGQP9JD5Y7&psc=1&refRID=0XJTZ9WNA1RGQP9JD5Y7
https://www.amazon.com/Elements-Causal-Inference-Foundations-Computation-ebook/dp/B078X5GRXD
https://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl-ebook/dp/B01B3P6NJM

Causal Inference: What If (the book)

Series: Challenges in Machine Learning

Talk

1. Yoshua Bengio’s IEEE Spectrum interview:

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

2. Yoshua Bengio’s NeurIPS 19 talk

From System 1 Deep Learning to System 2 Deep Learning

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

Learning Representation using Causal Invariance

ICLR 2019 Conference Videos 534780673594799.mp4

4. Judea Pearl’s interview at CogSci 2011

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

Judea Pearl, 2011 ACM Turing Award Recipient

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

The Mechanization of Causal Inference

Judea Pearl, 2012 ACM A.M. Turing Award Lecture "The Mechanization of Causal Inference"

6. Judea Pearl’s talk at AI Podcast

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

Judea Pearl: Causal Reasoning, Counterfactuals, and the Path to AGI | Lex Fridman Podcast #56

7. IDSS Distinguished Seminar Speaker Susan Athey

Machine Learning and Causal Inference - IDSS

IDSS Distinguished Seminar Speaker Susan Athey - Spring 2018

[LeiZhang]

1. Causal Discovery with Reinforcement Learning | OpenReview

• ICLR 2020 triple 8 rating paper
• Causal Discovery on static DAG by using RL

2. [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

3. https://dl.acm.org/doi/10.1145/3357384.3357864 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

4. 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