# Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling

arXiv: https://arxiv.org/abs/2208.09862 · HTML: https://mlsanity.com/paper/2208.09862/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 21 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Re-evaluating Word Mover's Distance](https://mlsanity.com/paper/2105.14403/) (2021)
- [Predicting long-term publication impact through a combination of early citations and journal impact factor](https://mlsanity.com/paper/1909.08907/) (2019)
- [Core Elements in the Process of Citing Publications: A Conceptual Overview of the Literature](https://mlsanity.com/paper/1707.02283/) (2017)
- [Inferring the causal effect of journals on citations](https://mlsanity.com/paper/1912.08648/) (2019)
- [Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks](https://mlsanity.com/paper/2003.03670/) (2020)
- [Poincare: Recommending Publication Venues via Treatment Effect Estimation](https://mlsanity.com/paper/2010.09157/) (2020)

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

- [Interestingness First Classifiers](https://mlsanity.com/paper/2508.19780/) (2025)

## Abstract (arXiv)

The research process includes many decisions, e.g., how to entitle and where to publish the paper. In this paper, we introduce a general framework for investigating the effects of such decisions. The main difficulty in investigating the effects is that we need to know counterfactual results, which are not available in reality. The key insight of our framework is inspired by the existing counterfactual analysis using twins, where the researchers regard twins as counterfactual units. The proposed framework regards a pair of papers that cite each other as twins. Such papers tend to be parallel works, on similar topics, and in similar communities. We investigate twin papers that adopted different decisions, observe the progress of the research impact brought by these studies, and estimate the effect of decisions by the difference in the impacts of these studies. We release our code and data, which we believe are highly beneficial owing to the scarcity of the dataset on counterfactual studies.