# Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community

arXiv: https://arxiv.org/abs/2110.07938 · HTML: https://mlsanity.com/paper/2110.07938/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 60 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [Causal Discovery Toolbox: Uncover causal relationships in Python](https://mlsanity.com/paper/1903.02278/) (2019)
- [Causal Effects of Linguistic Properties](https://mlsanity.com/paper/2010.12919/) (2020)
- [Distinguishing cause from effect using observational data: methods and benchmarks](https://mlsanity.com/paper/1412.3773/) (2014)
- [Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis](https://mlsanity.com/paper/1802.05046/) (2018)
- [Challenges of Using Text Classifiers for Causal Inference](https://mlsanity.com/paper/1810.00956/) (2018)
- [The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data](https://mlsanity.com/paper/1910.05387/) (2019)
- [Concave Penalized Estimation of Sparse Gaussian Bayesian Networks](https://mlsanity.com/paper/1401.0852/) (2014)
- [Estimating the Causal Impact of Recommendation Systems from Observational Data](https://mlsanity.com/paper/1510.05569/) (2015)
- [The Diversity-Innovation Paradox in Science](https://mlsanity.com/paper/1909.02063/) (2019)
- [Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference](https://mlsanity.com/paper/2009.09961/) (2020)
- [A Review on Algorithms for Constraint-based Causal Discovery](https://mlsanity.com/paper/1611.03977/) (2016)
- [Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data](https://mlsanity.com/paper/1805.03094/) (2018)
- [VAINE: Visualization and AI for Natural Experiments](https://mlsanity.com/paper/2109.04348/) (2021)
- [CL Scholar: The ACL Anthology Knowledge Graph Miner](https://mlsanity.com/paper/1804.05514/) (2018)
- [Predictability limit of partially observed systems](https://mlsanity.com/paper/2001.06547/) (2020)
- [Predicting and Explaining Behavioral Data with Structured Feature Space Decomposition](https://mlsanity.com/paper/1810.09841/) (2018)
- [Mapping Three Decades of Intellectual Change in Academia](https://mlsanity.com/paper/2004.01291/) (2020)

## Built on by (most cited first, 1 of 1)

- [Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers](https://mlsanity.com/paper/2307.09665/) (2023)

## Abstract (arXiv)

Drawing causal conclusions from observational real-world data is a very much desired but challenging task. In this paper we present mixed-method analyses to investigate causal influences of publication trends and behavior on the adoption, persistence, and retirement of certain research foci -- methodologies, materials, and tasks that are of interest to the computational linguistics (CL) community. Our key findings highlight evidence of the transition to rapidly emerging methodologies in the research community (e.g., adoption of bidirectional LSTMs influencing the retirement of LSTMs), the persistent engagement with trending tasks and techniques (e.g., deep learning, embeddings, generative, and language models), the effect of scientist location from outside the US, e.g., China on propensity of researching languages beyond English, and the potential impact of funding for large-scale research programs. We anticipate this work to provide useful insights about publication trends and behavior and raise the awareness about the potential for causal inference in the computational linguistics and a broader scientific community.