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

TL;DR

This paper introduces a novel framework using 'twin papers' to analyze the causal effects of publication decisions on research impact, addressing the challenge of counterfactual analysis in scholarly publishing.

Contribution

It proposes a simple, general framework inspired by twin studies to estimate the impact of publication decisions by comparing pairs of related papers with different choices.

Findings

Framework effectively estimates causal effects of publication decisions.

Provides a dataset and code for counterfactual analysis in scholarly impact studies.

Demonstrates the utility of twin-based causal inference in research evaluation.

Abstract

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.