DeepCausality

This is an attempt to stay on top of the ever increasing number of papers that combine flexible neural function approximators (aka neural networks) and causality research. I tried to organize the papers into a small number of categories. Some papers are part of multiple categories. If you happen to come across this list and want to add a paper please send me a pull request.

Approximating SCMs

Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath. 2017. CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training. https://arxiv.org/abs/1709.02023

Nick Pawlowski, Daniel C. Castro, Ben Glocker. 2020. Deep Structural Causal Models for Tractable Counterfactual Inference. https://arxiv.org/abs/2006.06485

Saloni Dash, Amit Sharma. 2020. Counterfactual Generation and Fairness Evaluation Using Adversarially Learned Inference. https://arxiv.org/abs/2009.08270

Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen. 2020. Causal Autoregressive Flows. https://arxiv.org/abs/2011.02268

Raha Moraffah, Bahman Moraffah, Mansooreh Karami, Adrienne Raglin, Huan Liu. 2020. Causal Adversarial Network for Learning Conditional and Interventional Distributions. https://arxiv.org/abs/2008.11376

Estimating the treatment effect from observational data/unobserved confounding

Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling. 2017. Causal Effect Inference with Deep Latent-Variable Models. https://arxiv.org/abs/1705.08821

Yunzhe Li, Kun Kuang, Bo Li, Peng Cui, Jianrong Tao, Hongxia Yang, Fei Wu. 2020. Continuous Treatment Effect Estimation via Generative Adversarial De-confounding. http://proceedings.mlr.press/v127/li20a.html

Pengzhou Wu, Kenji Fukumizu. 2021. Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning. https://arxiv.org/abs/2101.06662

Meta Learning and causality

Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, Bin Yu. 2017. Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning. https://arxiv.org/abs/1706.03461

Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal. 2019. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. https://arxiv.org/abs/1901.10912

A causal interpretation of deep learning methods

Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing, Charles Blundell. 2020. Representation Learning via Invariant Causal Mechanisms. https://arxiv.org/abs/2010.07922

Maximilian Ilse, Jakub M. Tomczak, Patrick Forré. 2020. Selecting Data Augmentation for Simulating Interventions. https://arxiv.org/abs/2005.01856

Domain adaptation and generalisation