As a social scientist, I spend much of my working life sorting out spurious claims about cause and effect. In any social science, particularly when it is impossible to adequately control many variables such as through an experimental design, the analysis and attribution of causality is inherently problematic. Too often, that is not the case in reporting the news, where causal assumptions are plentiful.
In thinking about why that might be the case, I would suggest consideration of some journalistic, social science, and political forces shaping journalistic practices.
Journalistically, one factor might well be the value of a clear narrative for a news story. For example, the UK’s public financial squeeze could be attributed to the interaction of a complex array of local and global developments, such as the global COVID-19 pandemic, the pandemic’s role in shaping work and productivity, the War in Ukraine, rising energy costs, inflation, rising interest rates around the world, and more. However, a story about public finances in the UK being a consequence of the ‘moron premium’ of the last ‘mini-budget’ or a ‘dullness dividend’ of different prime ministers is a more common story.[1] Another example from my own field of internet studies, is the attribution of nearly every psychological or behavioural problem of children being caused by social media or an ‘algorithm’.[2]
Journalistic practices can support this causality bias. For example, when journalists are developing a news story, they often look for scientists who can support their narrative. And there are many to choose from. I have fielded calls from journalists essentially asking if I might be able to support a particular claim of their story. “Do you agree that …?”. If, I say, as I do, that it is not that simple, then the chances of an interview are greatly diminished.
The incentives embedded in science communication could be another explanatory force. Simplified causal explanations are not only driven by journalistic demands, but also academics being prodded to have better outreach – often meaning exposure in the news media. Coaching academics to be interviewed by journalists is routinely focused on keeping their story short, simple, and clear. Nothing clearer than a good causal statement about x being caused by y. Don’t complicate the message. There is a threat that academics who want to get more credit for their work are likely to over-simplify their findings or at the worst, finding arguments to support the narrative of the journalist interviewing them.
Politically, another factor is the ways in which causal explanations can play well with what I would call the ‘politics of blame’. What actors should take the blame for different problems? Should it be the central bank, big tech, social media, Elon Musk, Russia, Putin, Ukraine, Ukraine fatigue, Biden, or who? You can readily see how the identification of a causal claim can justify placing blame on a particular decision by a particular actor.
Are politics, journalism, and the social sciences working together to amplify over-simplified causal explanations that often lack serious critical scrutiny.
To be clear, the reporting of causal claims that lack sufficient analytical support is entirely different from so-called fake news or fabricating a false narrative – Kremlin style. In contrast, it is a subtle bias in most cases of over-simplifying, over-stating evidence, or choosing to ignore alternative explanations.
Of course, causal claims by journalists can provide a never-ending stream of assertions that could be critically examined by social scientists. Whether positive or negative claims about causality, the job of a social scientist is to examine them analytically and critically rather than take them for granted. If we avoid attacking a straw man, that is an obvious fallacy, then social scientists – scientists of all disciplines – have been and could become even more useful antidotes and help counter causality journalism.
[1] https://www.ft.com/content/1dc057df-2ef0-47fa-bd40-3f5d229b0cff
Excellent analysis.