(Note that Causal Inference is highly related to Awesome Explainable AI, another similar collection Overview of Causal Inference)
Tutorial
 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 NeymanRubinHolland Theory
 Causal inference in statistics: An overview (by Judea Pearl)
 An Introduction to Causal Inference (by Judea Pearl)
 HernĂˇn MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
 Inferring causality in time series data
Course
Collection
Researcher
 Judea Pearl
 Leon Bottou
 Peter BĂĽhlmann
 Bernhard SchĂ¶lkopf
 David Blei
 Aidong Zhang
 Kun Kuang
 Peng Cui
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 MetaTransfer 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 DataDriven Variable Decomposition
 IJCAI 19: On the Estimation of Treatment Effect with Text Covariates
 Who Make Drivers Stop? Towards Drivercentric Risk Assessment: Risk Object Identification via Causal Inference

Deep Learning
 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 PropensityDropout
 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

Graph
 WSDM 20: Learning Individual Causal Effects from Networked Observational Data
 ExplaiNE: An Approach for Explaining Network Embeddingbased 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 timeseries 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
Journal
Book
https://www.amazon.com/ElementsCausalInferenceFoundationsComputationebook/dp/B078X5GRXD
Series: Challenges in Machine Learning
Talk
Causality is very important for the next steps of the progress of machine learning.
From System 1 Deep Learning to System 2 Deep Learning
 Leon Bottou 's keynote at ICLR on causality.
Learning Representation using Causal Invariance
 Judea Pearlâ€™s interview at CogSci 2011
â€śThere is no free will, which is a useful illusionâ€ť
 Judea Pearl, 2012 ACM A.M. Turing Award Lecture
The Mechanization of Causal Inference
 Judea Pearlâ€™s talk at AI Podcast
Causal Reasoning, Counterfactuals, Bayesian Networks, and the Path to AGI
 IDSS Distinguished Seminar Speaker Susan Athey