"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"

Gustav Sir; Leonardo Christov Moore; Martin Krutsky; Vyacheslav Kungurtsev

REVIEW 3 major objections 6 minor 67 references

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Causal machine learning redefines 'cause' and overstates what statistical graphs can certify, a philosophical critique argues.

desk verdict A well-read philosophical essay whose descriptive core about domain-specific causal language largely works, but whose strong conclusion that causal ML cannot certify causes rests on an under-defended ordinary-language premise. read the letter →

arxiv 2501.05844 v3 pith:5RTXYI2L submitted 2025-01-10 cs.LG

Vyacheslav Kungurtsev , Leonardo Christov Moore , Gustav Sir , Martin Krutsky This is my paper · ORCID

classification cs.LG

keywords causalitycausalmachinelearningdiscoveryordinarylanguagephilosophygamesepistemologyscientificdomainshermeneutics

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the word 'cause' has no single formal definition that a general-purpose computational method can capture. Instead, what counts as a cause depends on the scientific domain: physics expresses causes as terms in equations, biology requires consistent mechanisms across scales, and the social sciences rely on interpretive narratives. Because of this, the paper concludes that a discovered directed acyclic graph from conditional independence tests should not be read as demonstrating that 'A causes B' in any domain. This matters because causal machine learning is being used in medicine and policy, and overstating what its graphs certify could mislead real decisions. The paper proposes that definitive causal claims about open systems require convergent evidence across multiple scientific language games.

What carries the argument

The central machinery is the Ordinary Language method, drawn from Wittgenstein's language-game analysis, which treats the meaning of 'cause' as its actual use within each scientific community's practices. This method lets the paper show that the grammar of causal claims differs across physics, biology, and social science, and it uses Lakatos's 'hard core' idea to locate causal mechanisms within each domain's foundational assumptions. The argument then proceeds by contrasting this domain-relative meaning with the graphical independence model of causal learning, which the paper says amounts to redefining the word 'cause'.

What would settle it

A single well-documented case where a causal DAG discovered purely from conditional independence in an open biological or social system led to a verified intervention that the field's mechanistic theories had rejected, and where the mechanism was later confirmed, would directly contradict the paper's claim that independence-based discovery cannot certify real causes outside physics and engineering.

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Extended reading notes

Core claim

The authors discover that causality is a functional concept whose form is fixed by the grammatical conventions of each scientific community, not a single relation that statistical independence can certify. They argue that causal learning takes the exact equation systems of physics as an analogy and applies that analogy to open, emergent, and interpretive systems where it does not carry the same authority. Consequently, a fitted DAG with passing conditional-independence tests is at best a plausibility suggestion, not a certified cause. Definitive causality requires what the authors call an agglomeration of consistent evidence across domains, scales, and narrative forms.

Load-bearing premise

The argument depends on the premise that the everyday meaning of 'cause' used inside each scientific community is the correct standard for what a true causal claim must mean, which is a normative interpretive choice rather than a mathematical fact.

Editorial extensions

If this is right

Causal discovery results on real datasets should be reported as generating hypotheses, not as certified causes.
Research evaluating causal machine learning should demand domain-specific mechanistic validation before accepting a graph as evidence of causation.
The replication crisis in social science will not be fixed by more sophisticated statistics alone, but by lowering confidence and seeking convergence across multiple disciplines.
Causal statements in physics and engineering remain legitimate where the equation models are known and validated.
The burden of proof in medicine and policy shifts from a single DAG or p-value to multi-scale, multi-domain evidence.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A concrete test of the paper's position would be to run causal discovery on well-understood physics benchmarks versus open biological or social datasets and compare whether the discovered graphs align with the domains' known mechanisms.
The argument implies a communication standard for high-stakes 'causal AI': outputs should be phrased as 'consistent with' rather than 'causes' unless the domain mechanism is known and identified.
The authors' sketch of analogical reasoning via CP-logic suggests a research program for formalizing cross-domain evidence integration, which could be tested by building systems that transfer causal rules between modeled domains.
The critique can be read as a call for pluralistic causal models, where physics, biology, and social science each get a distinct causal calculus rather than one universal statistical framework.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is best read as an essay, not a technical result. What is genuinely new is the ordinary-language framing paired with a three-way demarcation: physics and engineering have closed equation systems where causal claims can be complete; biology requires consistency across scales because of emergence; social science offers hermeneutic and instrumental value even without precise predictive models. The smoking example is a good illustration of how causal consensus actually forms in medicine, and the paper is honest enough to concede that causal ML tools are not worthless when the equation form is right. That is real credit.

The soft spots are proportionate but not trivial. The main one is the load-bearing premise: the paper asserts that the customary use of “cause” inside each scientific language game is the correct standard for what causal claims must mean, and never really argues for it. Absent that defense, the strong claim in Section 6.2 that conditional-independence causal discovery “nor can it” certify causes does not follow. The paper itself defines cause as “the mechanism underlying fundamental forces of influence,” which is close to what structural causal models formalize; so the graphical model is better seen as an abstraction of that notion, not a redefinition. Once you frame it that way, the remaining dispute is about the right level of abstraction, which is a pragmatic matter, not a refutation. The stress-test note lands.

There are also a few empirical assertions without support: the claim that the authors could not find one real-data causal learning paper with all conditional-independence tests passing, the claim that few social-science DAG papers exist without type-1 error concerns, and the “99% of the exact function is known” figure. Minor in an essay, but should be fixed. The abstract also ends with a grammatical fragment, which needs rewriting.

The argument is otherwise coherent and the literature engagement is broad and mostly fair. It is not a new technical result, and the central critical content partly overlaps with Eberhardt and Peters et al., but the domain taxonomy and the hermeneutics framing are a real contribution to how causal ML results get communicated.

For whom? Philosophers of science, causal ML researchers worried about overclaiming, and anyone teaching responsible use of causal methods. It deserves a serious referee, not a desk reject. If I were the editor, I would send it out with a referee instruction to focus on the ordinary-language authority premise and the unsupported empirical claims; with those addressed, it is publishable in a venue that welcomes philosophical critique of ML.

Referee Report

3 major / 6 minor

Summary. This is an argumentative, interdisciplinary paper that critiques the foundations of causal machine learning (causal inference/discovery as implemented via Bayesian networks, DAGs, and conditional independence tests). The authors argue, using ordinary language philosophy, that the word "cause" has no single formal definition; its meaning is fixed by the language games of distinct scientific communities. They then demarcate physics/engineering (where mathematical models can fully capture causality), biology (where emergence requires multi-scale evidence), and the social sciences (where hermeneutic interpretation is valuable but precise causal claims require multi-domain convergence). Their central critical claim is that DAG-based causal learning should not be read as certifying that 'A causes B', and they argue that definitive causal claims about open systems require an agglomeration of evidence across multiple domains and levels of abstraction.

Significance. If its central argument were fully supported, the paper would be a significant contribution to the ongoing debate about the overinterpretation of causal machine learning results. Its main strengths are its synthetic use of philosophy of science (Wittgenstein, Kuhn, Lakatos, Quine) to frame the issue, its clear typology of causal semantics across physics, biology, and social science, and its explicit concession that causal formalism is correct for physics/engineering when the underlying equation model is known and exact. The paper also makes a useful practical recommendation: that causal discovery results in open, complex systems should be treated as hypothesis-generating rather than as definitive certification. The paper does not claim to provide mathematical proofs; its value is conceptual, and its credibility hinges on the normative premise about the authority of ordinary language in fixing the meaning of 'cause'.

major comments (3)

[§3.4–§3.5, §6.2] The paper moves from the descriptive observation that 'cause' is used differently in different scientific domains to the normative conclusion that the ordinary-language usage within each scientific tribe fixes what a causal claim must mean. This premise is never defended. The strongest statement in §6.2 that DAG-based causal discovery 'nor can it' certify causes, and in §3.5 that it 'amounts to redefining the word "cause"', depends entirely on that premise. Furthermore, the paper's own functional definition in §1—'the mechanism underlying fundamental forces of influence'—is precisely what a structural causal model's assignment X_i := f_i(Pa_i, U_i) formalizes. The graphical model is therefore not a redefinition but an abstraction of the paper's own concept; the remaining dispute is about whether the level of abstraction preserves the domain-relevant mechanisms, which is a pragmatic and gradable epistemic question. The categorical 'cannot' is not supported. I recommend reframing the conclusion as a counsel of caution about overclaiming from DAG+CI in open systems, rather than an in-principle impossibility.
[§6.3, §3.3] The positive thesis that definitive causal claims require an 'agglomeration of consistent evidence across multiple domains' is asserted but not operationalized. If, as the paper argues with Kuhn in §3.3, scientific paradigms are incommensurable language games, then it is nontrivial to say what counts as consistency of evidence across those games. The examples (smoking, Weber's Protestant Ethic, cognitive science) are suggestive, but no criterion is given for when cross-domain results harmonize rather than merely coexist. Without such a criterion, the proposed mixed-methods framework is underspecified as a research program.
[§2.1 and §6.2] Several empirical generalizations are made without supporting evidence. The claim in §2.1 that the authors could not identify a single real-data causal-learning paper with all conditional independence tests passing is anecdotal, not a systematic survey. Similarly, §6.2 states that sparse causal models in social sciences are 'uncommon in the literature' and that the few that exist 'present type-1 error concerns', but no citation is given. These empirical claims are used to bolster the critique of causal learning's practical value and should either be substantiated with a proper literature review or removed.

minor comments (6)

[Abstract] The final sentence of the abstract is a fragment: 'Given the role of epistemic hubris ... optimizing integration of different findings.' It needs a main clause to be a complete sentence.
[§3.4] The name 'Halpern' is misspelled as 'Harpen' twice in the discussion of actual causality.
[§5.3] 'paropagation' should be 'propagation'.
[§5.4] 'feedforwark' should be 'feedforward'.
[§6.2] The quotation 'bewitchment of intelligence by language' is a paraphrase of Wittgenstein's Philosophical Investigations §109; an exact citation would be helpful.
[§2.1] The phrase 'temporal difference in differences at interventions' is unclear and should be clarified.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the central philosophical claims are argued from independent sources, and the only self-citations are to the authors' own extended version for auxiliary detail.

full rationale

This paper is a philosophical critique, not a formal derivation chain. It contains no fitted parameters, no quantitative predictions, and no theorem whose conclusion is assumed in its premises. The central thesis—that "cause" has distinct grammatical forms across scientific domains while serving a common functional role—is argued from ordinary-language observations and from external philosophical authorities (Wittgenstein, Kuhn, Quine, Gadamer, Lakatos), not from the conclusion itself. The only self-citations are references to the authors' extended version (Kungurtsev et al., 2025) for additional formulations and a taxonomy of cognitive science subdomains; these are ancillary and not load-bearing. No uniqueness theorem or exclusion result is imported from the authors' prior work. The strongest skeptical objection is that the impossibility claim—that causal machine learning "cannot" certify causes and "amounts to redefining the word 'cause'"—depends on an asserted normative premise that ordinary language inside each scientific tribe fixes the legitimate meaning of "cause." That is a substantive epistemic criticism, but it is not circularity: the paper does not define "cause" as whatever causal ML cannot certify; it attempts to establish its semantic thesis from usage and then draws consequences. One might also note an internal tension: the paper's own functional definition of cause as "the mechanism underlying fundamental forces of influence" is close to what structural causal models formalize, which could undercut the critique, but tension is not self-derivation. Overall, the argument is self-contained in the sense required here, with only a minor, non-load-bearing self-citation, so the circularity score is low.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The paper has no fitted parameters because it has no quantitative model. Its free parameters are conceptual: the chosen domain boundaries. Its axioms are the interpretive commitments of the ordinary language method and several empirical generalizations that are stated with thin support. The only invented entity is hermeneutic truth, which is a philosophical category rather than a physical or mathematical object.

free parameters (1)

domain demarcation thresholds = physics/engineering, biology, social sciences
The three-way split of scientific domains is chosen by the authors to organize the argument. The boundaries are not derived from data or from a formal theory. The placement of fields into categories is a hand-selected classification and the argument depends on it.

assumptions (4)

domain assumption Ordinary language use is the correct standard for philosophical meaning.
The entire method presupposes that studying how scientists and laypeople use the word 'cause' reveals what the word genuinely means. This is stated in Section 3.4 and is not argued for at length.
domain assumption Scientific disciplines function as separate language games with distinct grammars of causality.
The claim that each scientific tribe has its own causal language game is taken from Wittgenstein and applied to modern sciences. The paper does not justify why domain language games are incommensurable enough to block cross-domain causal claims.
domain assumption A spurious statistical association can never be upgraded to a causal relation without mechanistic narrative.
The smoking case study in Section 4.2.2 is used to show that statistics alone are not enough. The paper generalizes from this case without a formal argument that mechanistic narrative is necessary in all open systems.
domain assumption Psychotherapy outcome equivalence across modalities is an established empirical fact.
The claim that all therapy modalities are similarly effective is cited with one 1996 gambling study. This is a much stronger empirical claim than the single citation supports, and the paper builds its hermeneutic account of psychology on it.

invented entities (1)

hermeneutic truth as a distinct epistemic category
purpose: Used to explain how social science and psychotherapy can be useful and even 'true' without predictive accuracy.
The paper introduces this as a way to keep social science knowledge valuable despite poor predictive power. It has no falsifiable handle outside the paper, since it is defined as a kind of truth validated by subjective benefit.

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Pith. "Pith review of "Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"." pith.science (2026). https://pith.science/paper/5RTXYI2L

@misc{pith2026250105844,
  author       = {Pith},
  title        = {Pith review of: "Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5RTXYI2L}},
  note         = {Machine review of arXiv:2501.05844}
}

read the original abstract

Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising specific computational techniques to apply to datasets that reveal the true nature of cause and effect in a number of important domains. In this paper we consider the epistemology of recognizing true cause and effect phenomena. We apply the Ordinary Language method of engaging on the customary use of the word 'cause' to investigate valid semantics of reasoning about cause and effect. We recognize that the grammars of cause and effect are fundamentally distinct in form across scientific domains, yet they maintain a consistent and central function. This function can best be described as the mechanism underlying fundamental forces of influence as considered prominent in the respective scientific domain. We demarcate 1) physics and engineering as domains wherein mathematical models are sufficient to comprehensively describe causality, 2) biology as introducing challenges of emergence while providing opportunities for showing consistent mechanisms across scale, and 3) the social sciences as introducing grander difficulties for establishing models of low prediction error but providing, through Hermeneutics, the potential for findings that are still instrumentally useful to individuals. We posit that definitive causal claims regarding a given phenomenon (writ large) can only come through an agglomeration of consistent evidence across multiple domains. This presents important methodological questions as far as harmonizing between language games and emergence across scales. Given the role of epistemic hubris in the contemporary crisis of credibility in the sciences, exercising greater caution as far as communicating precision as to the real degree of certainty certain evidence provides for rich collections of open problems in optimizing integration of different findings.

Figures

Figures reproduced from arXiv: 2501.05844 by the authors.

**Figure 2.** Probability tree for the CP-logic rule, illustrating the [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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