[ This is a Work In Progress (WIP) package ]
This package will have all the required resources related to the application of causal theory in NLP.
[1] Pearl, Judea (2009-09-14). Causality. Cambridge University Press. ISBN 9781139643986
[2] Elements of Causal Inference, Foundations and Learning Algorithms By Jonas Peters, Dominik Janzing and Bernhard Schölkopf
[1] CausaLM: Causal Model Explanation Through Counterfactual Language Models, Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart (https://arxiv.org/pdf/2005.13407v4.pdf)
[2] Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals, Yanai Elazar, Shauli Ravfogel, Alon Jacovi1 and Yoav Goldberg
[3] Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias, Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, Stuart Shieber
[4] Causal-BERT : Language models for causality detection between events expressed in text (https://arxiv.org/pdf/2012.05453v2.pdf)
[5] Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey (https://arxiv.org/pdf/1605.07895v1.pdf)
[7] Counterfactual VQA: A Cause-Effect Look at Language Bias (https://arxiv.org/pdf/2006.04315v4.pdf)
[8] Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation (https://arxiv.org/pdf/2101.04966v1.pdf)