The Missing Cause: An Analysis of Causal Attributions in Reporting on P alestine (2025)

Abstract: Missing cause bias is a specific type of bias in media reporting that relies on consistently omitting causal attribution to specific events, for example when omitting specific actors as causes of incidents. Identifying these patterns in news outlets can be helpful in assessing the level of bias present in media content. In this paper, we examine the prevalence of this bias in reporting on Palestine by identifying causal constructions in headlines. We compare headlines from three main news media outlets: CNN, the BBC, and AJ (AlJazeera), that cover the Israel-Palestine conflict. We also collect and compare these findings to data related to the Ukraine-Russia war to analyze editorial style within press organizations. We annotate a subset of this data and evaluate two causal language models (UniCausal and GPT-4o) for the identification and extraction of causal language in news headlines. Using the top performing model, GPT-4o, we machine annotate the full corpus and analyze missing bias prevalence within and across news organizations. Our findings reveal that BBC headlines tend to avoid directly attributing causality to Israel for the violence in Gaza, both when compared to other news outlets, and to its own reporting on other conflicts.

## introduction media reporting of conflict is often perceived by various stakeholders as biased. headlines, in particular, are frequently criticized for being misleading, incomplete, or lacking context, which can skew the information presented. just last year, the bbc reported more than 1,500 complaints over israel-palestine coverage, being accused of bias from both sides of the conflict 1 . a significant source of contention is the lack of causal attribution in events. missing cause bias @xcite , is a special type of information omission that consistently omits attributing responsibility, placing blame or giving praise to specific acts or actors that caused an event, such as passively describing a violent attack by using sentences that do not contain a subject, or using the passive voice to avoid naming an actor. in the past year, the subject of missing cause bias has become controversial in the media's coverage of the israel-palestine conflict. in july 2024, activists decried the bbc's coverage of the death of mohammad bhar by using the passive voice and failing to mention the cause of his death 2 . in august 2024, the bbc changed headlines after criticism from the israeli foreign ministry for failing to mention the fact that a bombing was triggered after rockets where allegedly fired from gaza 3 . in this paper, we propose using a causal relation extraction model to machine annotate online newspaper headlines, to measure missing cause in news media. by using causal relation extraction models to perform span detection of cause and effect @xcite , we can quantify and compare assigned causes and omitted causes after matching articles that cover the same events. the main contributions of this paper include: ## methodology to measure and analyze missing cause bias, we prepared a data selection and model evaluation pipeline to machine-annotate newspaper headlines at scale. we focused on headlines, as opposed to full articles, as they are optimized for contextual effect and processing effort, while directing readers to construct the optimal context for interpretation @xcite . this aligns with recent research that has shown that people can make inference from causal explanations @xcite . in other words, headlines give just enough information for readers to reconstruct the news story via inference, and omitting or including causal attributions in the headlines directly allows for implicit biases to be communicated without further information. our data analysis and evaluation strategy can be divided into the following steps: ## discussion and conclusions in this paper, we explored the use of causal language in media reporting on israel and palestine and how its detection can act as an indicator of bias, offering a window into the subtle ways in which narratives are shaped. we compared headlines from three different media outlets, aj, bbc and cnn, pertaining to their reporting on the escalation of the israel-palestine conflict. we directly compared their reporting the israel-palestine conflict to their reporting the russia-ukraine conflict. using a state-of-the-art causal extraction method, we automatically classified the headlines as causal and non-causal. we further extracted the cause and effect spans of each of the headlines. a comparison shows a clear bias by omission on the part of the bbc israel-palestine reporting, and to a lesser extend to cnn's israel-palestine reporting, especially when compared to opposed to aj's reporting. furthermore, it showed a clear omission bias when comparing the bbc's reporting to its own reporting on the russia-ukraine conflict.