by Alvin Blackshear | Historian & Researcher <ablackshear@gmail.com>
There is a particular kind of intellectual vertigo that comes from watching a machine narrate the past with total confidence. Ask an AI system to explain why a revolution happened, why a movement gained momentum, or why a community's history unfolded as it did, and it will almost always answer. The prose will be fluent, the timeline will be clean, and the causal claims will arrive stated as fact rather than as argument. What the answer will rarely do is pause to ask whether the events it has linked together were actually connected at all, or whether they simply happened to occur near one another in time. This is the quiet crisis facing historical writing in an era of generative AI: the technology is exceptionally good at producing narratives that sound causal, and considerably less good at knowing whether those narratives are true.
Historians have spent more than a century developing tools to guard against exactly this failure. Long before anyone worried about large language models hallucinating a tidy story out of scattered facts, historians were worried about themselves doing the same thing. The discipline's entire methodological apparatus, footnotes, source criticism, peer review, historiographical debate, exists in large part to slow down the impulse to declare that because one thing followed another, the first caused the second. What AI-generated history reintroduces, at scale and with new fluency, is an old temptation: the temptation to mistake sequence for cause, and pattern for proof.
The Seduction of Sequence
Correlation is easy to notice and hard to interpret responsibly. Two trends move together, two events happen in the same decade, two figures cross paths, and the mind supplies a connection because connection is a more satisfying story than coincidence. Sarah Woodhouse's recent work on correlation reminds us that not all statistical relationships imply causation, and that there are several distinct varieties of correlation, only one of which reflects a genuine causal link between the variables involved.[1] The others might reflect a shared underlying cause, a coincidence of timing, a feedback loop running in the opposite direction from what intuition suggests, or simply noise that resembles a pattern if you squint at it long enough.
Historians face this same interpretive challenge, except their raw material is not a spreadsheet but the messy residue of human lives: letters, court records, newspapers, oral testimony, material culture. When two developments appear together in this record, a rise in labor unrest alongside a shift in immigration policy, a change in religious practice alongside an economic downturn, a legal victory alongside a change in public rhetoric, the historian's task is to ask what actually connects them, if anything does. Johannes Nagel's comparative analysis of causal reasoning in historical scholarship makes a useful observation here: historians rarely operate with the clean methodological categories that philosophers of science might prefer. Real historical practice is often described as vaguely causal-analytical, blending causal-intentional explanation, attention to unintended consequences, and selective use of theory and comparison, without ever fully resolving into a single rigorous method.[2] This is not a failure of the discipline. It is an honest reflection of how difficult the underlying problem actually is.
AI systems, by contrast, do not experience this difficulty as friction. A model trained to produce plausible continuations of text will happily supply a causal mechanism because causal mechanisms make for better sentences than admissions of uncertainty. The danger is not that AI invents facts out of nothing, though it sometimes does that too. The deeper danger is that it takes real facts, arranges them in a narrative shape, and lets the shape itself imply a causal claim that no historian has actually verified.
Mechanism, Not Just Chronology
If there is a single principle that separates serious historical causal argument from mere narrative sequencing, it is the requirement of an explicit mechanism. It is not enough to say that event A preceded event B and therefore contributed to it. A historian is expected to explain how A produced B: through what institutions, what decisions, what material pressures, what beliefs held by which people.
Daniel Little's work on the microfoundations of social explanation is instructive here. Little argues that sweeping historical forces such as capitalism, the state, or industrialization do not themselves act as causal agents in any direct sense. They are abstractions that describe the aggregate result of countless individual decisions, made by people operating within specific institutions and constrained by specific incentives and beliefs. A causal historical explanation, on this view, has to eventually cash out in terms of those individuals: what they knew, what they wanted, what options were actually available to them, and how their choices interacted with those of others to produce a larger outcome. This is demanding work. It requires the historian to move between scales, from the granular texture of a single decision to the broader pattern that many such decisions eventually create, without losing the thread that connects the two.[3]
Mark Hewitson's argument about the marginalization of causal thinking within the historical profession adds an important wrinkle to this picture. Hewitson suggests that the turn toward language, discourse, and representation that reshaped historical writing over the past several decades, whatever its genuine intellectual gains, also made many historians reluctant to speak plainly about why things happened. Causal explanation came to seem naive, or worse, complicit in older positivist ambitions to discover fixed laws of history. And yet, as Hewitson points out, historians never actually stopped making causal arguments. Every account of a revolution, an election, a migration, or a social movement is saturated with implicit claims about why it occurred. What was lost was not the practice of causal reasoning but the willingness to examine that practice openly and hold it to a clear standard.[4]
This matters enormously for evaluating AI-generated history, because a system that has absorbed decades of scholarship in which causal claims are made implicitly, without explicit justification, will likely reproduce that same implicitness. It will offer causal-sounding narratives without ever surfacing the mechanism that supposedly connects cause to effect, because the training material it draws from often does the same thing. The corrective, for both human historians and the tools they now use, is the same: state the mechanism, or admit that you cannot.
What Coincidence and Contingency Demand of Us
Not everything that happens in history happens for a deep structural reason. Some things happen because a particular person was in a particular place at a particular moment, because a letter arrived a day late, because an illness struck one leader and spared another. Historians call this contingency, and distinguishing it from genuine causation is one of the discipline's more delicate tasks.
The stakes of this distinction are not merely academic. When causal narratives are built where only coincidence existed, the resulting history can flatten the genuine unpredictability and agency that shaped real events into something that looks inevitable, as though it could only have happened the way it did. This has particular consequences for histories that have too often been treated as footnotes to a larger, supposedly more central narrative. African American history, feminist history, and the history of gay and queer communities have all, at various points, been subjected to accounts that either erase contingency entirely, presenting outcomes as foregone conclusions of impersonal social forces, or that erase causation entirely, treating hard-won structural change as a matter of isolated coincidence rather than sustained organizing and argument. Getting the distinction right, separating what was truly contingent from what was the product of deliberate, structural, causal effort, is not a technical nicety. It is a matter of according historical actors the accuracy they deserve, whether that means recognizing the genuine unpredictability they navigated or recognizing the genuine causal power of what they built.
Tay Jeong's work on counterfactual reasoning offers historians one of their sharpest tools for making this distinction. The basic move is to ask what would have happened in the absence of the proposed cause. If removing a particular event or decision from the historical record would plausibly have left the outcome largely unchanged, the causal claim weakens considerably. If removing it would plausibly have produced a genuinely different outcome, the causal claim gains support. Jeong's more technical contribution is to note that counterfactual reasoning is most useful not as a blanket method applied indiscriminately, but in specific situations, particularly where two or more candidate causes seem to be doing similar explanatory work and our untrained intuitions about which one truly mattered are likely to mislead us. This is precise, careful reasoning, and it is exactly the kind of reasoning that a fluent AI narrative can obscure simply by never raising the question in the first place.[5]
The Discipline of Alternative Explanations
Good historical argument does not simply assert a cause and move on. It considers what else might explain the same outcome, and it explains why the preferred causal account is more persuasive than the alternatives. This habit of mind, developed across the humanities, has an unexpected ally in fields that seem far removed from history. Hannah Correia and her coauthors, writing about causal inference in environmental research, describe systematic approaches for distinguishing genuine causal relationships from mere observational association, approaches built around identifying confounding variables and establishing plausible mechanisms before accepting a causal claim.[6] Linbo Wang's comparative overview of causal inference frameworks, spanning the traditions associated with Rubin, Pearl, and structural equation modeling, makes a similar point from a more statistical angle: rigorous causal reasoning requires explicit assumptions, and different frameworks make different assumptions visible in different ways.[7] Though these authors are not writing about history, their underlying discipline, of refusing to accept a causal story until alternatives have been seriously weighed, translates directly into the historian's craft.
This is precisely the discipline that AI-generated narratives tend to skip. A well-trained model can produce a fluent account of why a particular reform succeeded, but it rarely volunteers the alternative explanations a careful historian would consider and reject: that the reform succeeded for reasons largely unrelated to the actors typically credited, that its apparent success was partly an artifact of how success was later measured, or that multiple contributing factors were entangled in ways that resist a single tidy story.
Evidence, Uncertainty, and the Historian's Honesty
Underneath all of this sits the question of evidence. Every causal claim a historian makes rests on some body of evidence, and that evidence varies enormously in quality, completeness, and reliability. Xinyue Chen and colleagues, in their study of how historians actually use visualization in their published work, found that historians face persistent practical and epistemological barriers when trying to represent uncertainty and justify their conclusions to readers and peers.[8] The justification burden, as they describe it, is not a peripheral concern. It is central to what makes a piece of historical writing trustworthy.
Georg Iggers, reflecting late in his career on the historian's role as an engaged intellectual, insisted that this burden cannot be set aside even by historians who write from a place of moral or political conviction. Iggers rejected the idea that historians must choose between detached objectivity and partisan advocacy.[9] Instead, he argued for a middle position: historians can and should be engaged with the pressing concerns of their time, but that engagement must never come at the cost of rigorous evidentiary standards. A historian writing feminist history, or African American history, or the history of gay and queer life, is no less bound by these standards than any other scholar, and indeed the stakes of getting the causal story right, of neither overstating structural inevitability nor understating hard-won agency, are often higher precisely because these histories have so frequently been distorted or dismissed.
Frank Ankersmit pushes this even further by reminding us that the past itself no longer exists in any directly accessible form. Historians do not reproduce it; they represent it, using an inherited semantic apparatus of meaning, truth, and reference that Ankersmit argues must be handled with care rather than assumed to work automatically.[10] If historical writing is, at some level, an act of representation rather than simple description, then AI-generated historical narrative inherits this same burden. It too is representing the past, not retrieving it, and representation always involves choices about emphasis, framing, and inclusion that deserve scrutiny rather than passive acceptance.
A Standard Worth Holding
What emerges from this body of scholarship is not a single formula but a disposition, a way of approaching any historical claim, human-authored or machine-generated, with a consistent set of questions. Is there a stated mechanism connecting cause to effect, or only a sequence dressed up as causation? Have alternative explanations been seriously considered and set aside for identifiable reasons? Has the line between structural causation and genuine contingency been drawn carefully, rather than assumed? Would the outcome have looked meaningfully different if the proposed cause were absent? And does the account acknowledge the limits of its own evidence, rather than presenting a confident narrative where the underlying record is thin or contested?
These questions predate artificial intelligence by decades, and they will outlast any particular model or platform. What AI has changed is the volume and fluency with which causal-sounding narratives can now be produced, and therefore the urgency of applying these questions consistently. The historians whose work informs this discussion were not writing with AI in mind. They were writing because the problem of causation in history has always been genuinely hard, and because getting it wrong has always carried real costs, especially for the histories of communities whose stories have too often been told either as inevitable or as accidental, when the truth usually lies in the harder, more interesting space between. That space, between correlation and causation, between coincidence and contingency, between chronology and mechanism, is where careful historical work has always lived. It is also, increasingly, where the responsible use of AI in historical research will need to live too.
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Notes:
[1] Sarah Woodhouse, "When Two Things Seem Linked (but Aren't): Understanding Different Types of Correlations," Clearer Thinking, July 2, 2025, https://www.clearerthinking.org/post/when-two-things-seem-linked-but-aren-t-understanding-different-types-of-correlations.
[2] Johannes Nagel, "Causal Explanation in History: An Analysis of Research Strategies," Analyse & Kritik, March 12, 2026, https://doi.org/10.1007/s11577-026-01059-8.
[3] Daniel Little, Microfoundations, Method, and Causation: On the Philosophy of the Social Sciences (New Brunswick, NJ: Transaction Publishers, 1998).
[4] Mark Hewitson, History and Causality (Basingstoke: Palgrave Macmillan, 2014).
[5] Tay Jeong, "When to Use Counterfactuals in Causal Historiography," Sage Journals, February 2, 2025, https://doi.org/10.1177/00491241251314039.
[6] Hannah E. Correia et al., "Best Practices for Moving from Correlation to Causation in Environmental Research," Nature Communications, February 24, 2026, https://rdcu.be/fxl0y.
[7] Linbo Wang, "Causal Inference: A Tale of Three Frameworks," Journal of Data Science, February 11, 2026, https://doi.org/10.6339/25-JDS1211.
[8] Xinyue Chen et al., "How Historians Use Visualization: A Corpus-Backed Taxonomy and Analysis for Cross-Disciplinary Practice," July 10, 2026, https://doi.org/10.48550/arXiv.2605.01456.
[9] Georg Iggers, "The Historian as an Engaged Intellectual: Historical Writing and Social Criticism – A Personal Retrospective," in The Engaged Historian: Perspectives on the Intersections of Politics, Activism and the Historical Profession, ed. Stefan Berger (New York: Berghahn Books, 2019), 277–299.
[10] Frank Ankersmit, Historical Representation (Stanford, CA: Stanford University Press, 2002).
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Works Cited
Ankersmit, Frank. Historical Representation. Stanford, CA: Stanford University Press, 2002.
Chen, Xinyue, et al. "How Historians Use Visualization: A Corpus-Backed Taxonomy and Analysis for Cross-Disciplinary Practice." July 10, 2026. https://doi.org/10.48550/arXiv.2605.01456.
Correia, Hannah E., et al. "Best Practices for Moving from Correlation to Causation in Environmental Research." Nature Communications, February 24, 2026. https://rdcu.be/fxl0y.
Hewitson, Mark. History and Causality. Basingstoke: Palgrave Macmillan, 2014.
Iggers, Georg. "The Historian as an Engaged Intellectual: Historical Writing and Social Criticism – A Personal Retrospective." In The Engaged Historian: Perspectives on the Intersections of Politics, Activism and the Historical Profession, edited by Stefan Berger, 277–299. New York: Berghahn Books, 2019.
International Commission for the History and Theory of Historiography (ICHTH). 2026 Congress Proceedings. https://www.ichth.net/content/home.html.
Jeong, Tay. "When to Use Counterfactuals in Causal Historiography." Sage Journals, February 2, 2025. https://doi.org/10.1177/00491241251314039.
Little, Daniel. Microfoundations, Method, and Causation: On the Philosophy of the Social Sciences. New Brunswick, NJ: Transaction Publishers, 1998.
Nagel, Johannes. "Causal Explanation in History: An Analysis of Research Strategies." Analyse & Kritik, March 12, 2026. https://doi.org/10.1007/s11577-026-01059-8.
Wang, Linbo. "Causal Inference: A Tale of Three Frameworks." Journal of Data Science, February 11, 2026. https://doi.org/10.6339/25-JDS1211.
Woodhouse, Sarah. "When Two Things Seem Linked (but Aren't): Understanding Different Types of Correlations." Clearer Thinking, July 2, 2025. https://www.clearerthinking.org/post/when-two-things-seem-linked-but-aren-t-understanding-different-types-of-correlations.
