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Causality_Literature

links to conference publications in causality domain

Table of contents

  • 1️⃣ Survey Paper
  • 2️⃣ Foundamental Causality
  • 3️⃣ Causality in Machine Learning
  • 4️⃣ Causal Interpretability
  • 5️⃣ Causal Inference
  • 6️⃣ Confounding
  • 7️⃣ Privacy

Privacy

Uncategorized

Category Author Paper Pdf with comments summary code
7️⃣ Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet Privacy-Preserving Causal Inference via Inverse Probability Weighting [Link] -

Uncategorized

Category Author Paper Pdf with comments summary code
3️⃣ Phillip Lippe, Taco Cohen, and Efstratios Gavves Efficient Neural Causal Discovery without Acyclicity Constraints Link Code

ICML 2021

Category Author Paper Pdf with comments summary code
3️⃣ Lin, Wangyu, Hao Lan, Baochun Li Generative Causal Explanations for Graph Neural Networks Link Link -
2️⃣ Mastakouri, Atalanti A., Bernhard Schölkopf, and Dominik Janzing Necessary and sufficient conditions for causal feature selection in ime series with latent common causes [Link] [Link] -
3️⃣ Sumedh Sontakke, Arash Mehrjou, Laurent Itti, and Bernhard Schölkopf Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning [Link] [Link] -
2️⃣ Rui Chen, Sanjeeb Dash, and Tian Gao Integer Programming for Causal Structure Learning in the Presence of Latent Variables [Link] [Link] -
5️⃣ Amanda Gentzel, Purva Pruthi, and David Jensen How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference [Link] [Link] -
2️⃣ David A Bruns-Smith Model-Free and Model-Based Policy Evaluation when Causality is Uncertain [Link] [Link] -
2️⃣ Divyat Mahajan, Shruti Tople, and Amit Sharma Domain Generalization using Causal Matching [Link] [Link] -
2️⃣ Yonghan Jung, Jin Tian, and Elias Bareinboim Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning [Link] [Link] -
5️⃣ Jason Hartford, Victor Veitch, and Dhanya Sridhar Valid Causal Inference with (Some) Invalid Instruments [Link] [Link] -
6️⃣ Andrew Jesson, Sören Mindermann, Yarin Gal, and Uri Shalit Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding [Link] [Link] -
2️⃣ Michael Oberst, Nikolaj Thams, Jonas Peters, and David Sontag Regularizing towards Causal Invariance: Linear Models with Proxies [Link] [Link] -
2️⃣ A LOT Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction [Link] [Link] -
6️⃣ Elias Chaibub Neto Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners [Link] [Link] -

UAI 2021

Category Author Paper Pdf with comments summary code
4️⃣ Takeshi Teshima and Masashi Sugiyama Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation Link Link -
6️⃣ Takashi Nicholas Maeda and Shohei Shimizu Discovery of Causal Additive Models in the Presence of Unobserved Variables Link Link -
5️⃣ Eigil Fjeldgren Rischel and Sebastian Weichwald Compositional Abstraction Error and a Category of Causal Models Link Link -
2️⃣ David Strieder, Tobias Freidling, stefan Haffner, and Mathias Drton Confidence in Causal Discovery with Linear Causal Models Link Link -
2️⃣ Marcel Wienöbst, Max Bannach, Maciej Liśkiewicz Extendability of Causal Graphical Models: Algorithms and Computational Complexity Link Link -
5️⃣ Ludvig Hult, Dave Zachariah Inference of Causal Effects when Control Variables are Unknown Link Link -
5️⃣ Sofia Triantafillou, Fattaneh Jabbari, and Greg Cooper Causal Markov Boundaries Link Link -
2️⃣ Philip A. Boeken and Joris M. Mooij A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery Link Link -
5️⃣ Spencer Gordon, Vinayak M. Kumar, Leonard J. Schulman, and Piyush Srivastava Condition Number Bounds for Causal Inference Link Link -
5️⃣ Abhinav Kumar and Gaurav Sinha Disentangling Mixtures of Unknown Causal Interventions Link Link -

NeurIPS 2020

Category Author Paper Pdf with comments summary code
4️⃣ Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, and Isabel Valera Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Link Link -
3️⃣ Cheng Zhang, Kun Zhang, and Yingzhen Li A Causal View on Robustness of Neural Networks Link Link -
5️⃣ Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun Causal Intervention for Weakly-Supervised Semantic Segmentation Link Link Python
5️⃣ Nick Pawlowski, Daniel C. Castro, and Ben Glocker Deep Structural Causal Models for Tractable Counterfactual Inference Link Link Python
4️⃣ Christopher Frye, Colin Rowat, and Ilya Feige Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Link Link R
5️⃣ Samuel Håkansson, Viktor Lindblom, Omer Gottesman, and Fredrik D. Johansson Learning to search efficiently for causally near-optimal treatments Link Link Python
4️⃣ Yuval Atzmon, Felix Kreuk, Uri Shalit, and Gal Chechik A causal view of compositional zero-shot recognition Link Link Python
4️⃣ Trent Kyono, Yao Zhang, and Mihaela van der Schaar CASTLE: Regularization via Auxiliary Causal Graph Discovery Link Link Python
5️⃣ Kaihua Tang, Jianqiang Huang, and Hanwang Zhang Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect Link Link Python
4️⃣ Atalanti Mastakouri, Bernhard Schölkopf Causal analysis of Covid-19 Spread in Germany Link Link -
5️⃣ Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models Link Link -
6️⃣ Aahlad Puli, Adler Perotte, and Rajesh Ranganath Causal Estimation with Functional Confounders Link Link -
3️⃣ Matthew O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark Davenport Generative causal explanations of black-box classifiers Link Link Python
4️⃣ Lun Wang, Qi Pang, Dawn Song Towards practical differentially private causal graph discovery Link Link Python
4️⃣ Junsouk Choi, Robert Chapkin, Yang Ni Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks Link Link R
5️⃣ Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, and Javier González Multi-task Causal Learning with Gaussian Processes Link Link Python
4️⃣ Jussi Viinikka, Antti Hyttinen, Johan Pensar, and Mikko Koivisto Towards Scalable Bayesian Learning of Causal DAGs Link Link -
5️⃣ Aahlad Puli, Rajesh Ranganath General Control Functions for Causal Effect Estimation from IVs Link Link Python
3️⃣ Tianlin Xu, Li Kevin Wenliang, Michael Munn, and Beatrice Acciaio COT-GAN: Generating Sequential Data via Causal Optimal Transport Link Link Python
3️⃣ Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, and Animesh Garg Causal Discovery in Physical Systems from Videos Link Link Python
5️⃣ Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, and Elias Bareinboim Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning Link Link -
4️⃣ Ming Gao, Yi Ding, Bryon Aragam A polynomial-time algorithm for learning nonparametric causal graphs Link Link R
5️⃣ Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Link Link Python
6️⃣ Junzhe Zhang, Daniel Kumor, and Elias Bareinboim Causal Imitation Learning With Unobserved Confounders Link Link -
4️⃣ Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber Investigating Gender Bias in Language Models Using Causal Mediation Analysis Link Link -
6️⃣ Andreas Gerhardus, Jakob Runge High-recall causal discovery for autocorrelated time series with latent confounders Link Link -
5️⃣ Yonghan Jung, Jin Tian, Elias Bareinboim Learning Causal Effects via Weighted Empirical Risk Minimization Link Link -
5️⃣ Spencer Compton, Murat Kocaoglu, Kristjan Greenewald, and Dmitriy Katz Entropic Causal Inference: Identifiability and Finite Sample Results Link Link -
6️⃣ Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Link Link -
4️⃣ Juan L. Gamella and Christina Heinze-Deml Active Invariant Causal Prediction: Experiment Selection through Stability Link Link Python
5️⃣ Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, and Sriram Vishwanath Applications of Common Entropy for Causal Inference Link Link -
4️⃣ Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam Active Structure Learning of Causal DAGs via Directed Clique Trees Link Link Python
5️⃣ Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, and Alexandre Drouin Differentiable Causal Discovery from Interventional Data Link Link Python

AISTATS 2021

Category Author Paper Pdf with comments summary code
5️⃣ Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint Link Link -
3️⃣ Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien An Analysis of the Adaptation Speed of Causal Models Link Link Python
5️⃣ Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima Regret Minimization for Causal Inference on Large Treatment Space Link Link -
2️⃣ Shunsuke Horii Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions Link Link -
5️⃣ Chi Zhang, Carlos Cinelli, Bryant Chen, Judea Pearl Exploiting Equality Constraints in Causal Inference Link Link -
3️⃣ Vineet Nair, Vishakha Patil, Gaurav Sinha Budgeted and Non-budgeted Causal Bandits Link Link -
6️⃣ Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser Differentiable Causal Discovery Under Unmeasured Confounding Link Link -
5️⃣ Kyra Gan, Andrew Li, Zachary Lipton, Sridhar Tayur Causal Inference with Selectively Deconfounded Data Link Link -
5️⃣ Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze Yun Leong Causal Modeling with Stochastic Confounders Link Link -
3️⃣ Ilyes Khemakhem, Ricardo Monti, Robert Leech, Aapo Hyvarinen Causal Autoregressive Flows Link Link -
5️⃣ Yunpu Ma, Volker Tresp Causal Inference under Networked Interference and Intervention Policy Enhancement Link Link -

TODO

  • ICML
  • UAI
  • NeurIPS
  • AISTATS
  • Notes: New trend in differentiable causal discovery