• Feder et al. (2022) - Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (https://arxiv.org/pdf/2109.00725.pdf)
• Keith et al. (2020) - Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (https://arxiv.org/pdf/2005.00649.pdf)
• Pryzant et al. (2023) - Predicting Sales from the Language of Product Descriptions (https://nlp.stanford.edu/pubs/pryzant2017sigir.pdf)
• Veitch et al. (2020) - Adapting Text Embeddings for Causal Inference (https://arxiv.org/pdf/1905.12741.pdf)
• Willig et al. (2023) - Causal Parrots: Large Language Models May Talk Causality But Are Not Causal (https://www.matej-zecevic.de/research/assets/Causal_Parrots__Large_Language_Models_May_Talk_Causality_But_Are_Not_Causal_.pdf)
• Zhang et al. (2023) - Understanding Causality with Large Language Models: Feasibility and Opportunities (https://arxiv.org/pdf/2304.05524.pdf)
• Feder et al. (2023) - Causal-structure Driven Augmentations for Text OOD Generalization (https://openreview.net/pdf?id=XmTRYK1uN2)

Repos

• [REPO] Causal NLP papers (https://github.com/causaltext/causal-text-papers)
• [REPO] Causal BERT in PyTorch (https://github.com/rpryzant/causal-bert-pytorch)

Videos

• [VIDEO] Manzoor (2021) - Tutorial 3: Controlling for Text in Causal Inference with Double Machine Learning (https://www.youtube.com/watch?v=DwUqA1ydJI0)
• [VIDEO] Grimmer (2023) - How to Make Causal Inferences Using Text (https://www.youtube.com/watch?v=msPVye5ox4k)

CLADDER: A Benchmark to Assess Causal Reasoning Capabilities of Language Models

[EMNLP 2023 Workshop Keynote] Zhijing Jin: "Causal NLP: A Path towards Opening the Black Box of NLP" - YouTube

Proximal Causal Inference With Text Data

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Teaching Transformers Causal Reasoning through Axiomatic Training

C2P: Featuring Large Language Models with Causal Rea- soning