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REVIEW 2 major objections 4 minor 42 references

The Estimand Framework and Causal Inference: Complementary not Competing Paradigms

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The ICH E9(R1) estimand framework and causal inference define the same target effect and should be used together.

desk verdict Clear conceptual paper showing ICH E9(R1) and causal estimands map onto each other for four of the five intercurrent-event strategies; the unillustrated 'while on treatment' case is a revision-level gap, not a rejection. read the letter →

arxiv 2412.12380 v1 pith:YC54AB4W submitted 2024-12-16 stat.ME stat.AP

Thomas Drury , Jonathan W. Bartlett , David Wright , Oliver N. Keene This is my paper · ORCID
classification stat.MEstat.AP MSC 62D2062P10
keywords ICHE9(R1)estimandscausalinferencepotentialoutcomesintercurrenteventsclinicaltrialstreatmentpolicyprincipalstratum
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

This paper argues that the ICH E9(R1) estimands framework and the causal inference framework are not rivals but two ways of stating the same thing: a population-based summary of a treatment effect on an outcome. Using three real trial settings (diabetes, nasal polyps, COVID-19 vaccine), the authors show that each ICH estimand can be rewritten as a potential-outcomes expression, and that the ICH intercurrent-event strategies correspond to causal concepts such as effect of assignment, intervening on post-baseline variables, composite outcomes, and principal strata. If the authors are right, trialists can keep the ICH framework's descriptive table for communication while borrowing causal inference's mathematical precision and explicit assumptions for specification. That combination would make it easier to say precisely what a trial is estimating and why.

What carries the argument

The mapping between ICH intercurrent-event (IE) strategies and causal inference concepts is the load-bearing object. The paper identifies treatment policy with the effect of assignment, hypothetical with an intervention that sets post-baseline variables to specified values, composite with a redefined outcome that incorporates the event (with a penalty value), while-on-treatment with measuring the outcome only until the event, and principal stratum with the effect in the subpopulation that would or would not experience the event under both treatments. This correspondence is what allows the paper to claim that a single target effect can be expressed in either framework, and that the frameworks are complementary.

What would settle it

Take a real trial with an intercurrent event that is not a well-defined intervention, such as death as a competing risk for a quality-of-life endpoint, and try to write the ICH treatment-policy and hypothetical estimands as causal estimands in the paper's notation; if the formulations diverge or one is inexpressible, the claimed complementarity fails for that class of events.

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Extended reading notes

Core claim

The central claim is that 'estimand' in ICH E9(R1) and 'estimand' in causal inference denote essentially the same target: a comparison, in a specified population, of potential outcomes under specified treatment conditions. The paper demonstrates this by formulating the same clinical questions in both languages for three trials: the treatment-policy strategy matches the causal estimand that leaves post-baseline variables unintervened on; the hypothetical strategy matches a causal estimand that intervenes to set those variables (e.g., no discontinuation, no rescue); the composite strategy matches redefining the outcome as a mixture of the natural outcome and a penalty value; and the principal-stratum strategy matches conditioning on potential event status. The paper concludes that the two frameworks are complementary and encourages practitioners familiar with one to learn the other.

Load-bearing premise

The complementarity claim rests on the assumption that every intercurrent event can be mapped onto a causal concept—an intervenable post-baseline variable or a principal stratum—and for some events, such as death, that mapping may not be well defined.

Editorial extensions

If this is right

  • A trial team can present the ICH five-component table to stakeholders and the corresponding potential-outcome expression to statisticians, keeping both audiences clear.
  • Adding a causal graph to an ICH estimand makes the assumed relationships between treatment, intercurrent events, and outcome explicit rather than implicit.
  • The hypothetical strategy, read causally, is an effect under a specified intervention on adherence and rescue use, directly linking it to per-protocol effect definitions.
  • Principal-stratum estimands, the paper notes, are only estimable under strong and untestable assumptions, a point the ICH text does not spell out.
  • Adopting both vocabularies should reduce the confusion that arises when the single word 'estimand' is used in two partially overlapping senses.

Reading between the lines

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

  • The mapping works most cleanly for intercurrent events that can be framed as interventions; for terminal events like death, the causal language would need new machinery, suggesting the complementarity may be narrower than stated.
  • The paper's accessibility claim is untested; a survey asking clinical trialists to interpret both formulations would provide direct evidence for or against it.
  • A practical by-product could be a translation guide that pairs each ICH strategy with its causal estimand notation, going beyond the three examples given here.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. The paper compares the ICH E9(R1) estimands framework with the causal inference framework, using three clinical trial examples (diabetes, nasal polyps, COVID-19) to show how each can define estimands that address intercurrent events. The authors argue that, despite differences in form and precision, the two paradigms are complementary: the ICH framework provides a structured, descriptive approach for communicating trial objectives, while causal inference offers mathematical precision and explicit assumptions via potential outcomes and DAGs. They conclude that both can quantify a population-based summary of a target effect and encourage practitioners to use both frameworks in combination.

Significance. If the central claim holds, the paper provides a valuable didactic bridge between two important statistical frameworks, helping trialists and statisticians understand when and how ICH E9(R1) estimands correspond to causal estimands. Its strengths are the worked examples covering treatment policy, hypothetical, composite, and principal stratum strategies; the explicit acknowledgment of ambiguity in the ICH guidance; and the balanced discussion of limitations such as the untestable assumptions for principal strata and the arbitrariness of composite penalty values. The paper does not contain new mathematical results, but it offers a clear and practical framework for cross-communication, which is of genuine use to the clinical trials community.

major comments (2)
  1. [§3, §6.1, §7] The paper defines five intercurrent-event strategies in §3 but never revisits the "while on treatment" strategy in any of the examples, which cover only treatment policy, hypothetical, composite, and principal stratum strategies. The conclusion in §7 asserts broadly that both paradigms "can be used to quantify a population-based summary of a target effect on an outcome for a given population," and §6.1 states that an IE is conceptually the same as a post-baseline mediator or time-varying treatment. Without a discussion of how the while-on-treatment strategy maps to a causal estimand—for example, via dynamic treatment regimes or principal stratification—the complementarity claim is wider than the demonstrated evidence. Please add such a discussion or explicitly qualify the conclusion to the strategies actually illustrated.
  2. [§3, §6.1] The proposed mapping between intercurrent events and mediators or time-varying treatments is presented as "reasonably intuitive" and general, but it does not obviously accommodate death as an intercurrent event, which the paper itself lists in §3 as an important IE in some trials. When death occurs before the outcome measurement, the outcome is undefined in the usual sense, and neither a mediator nor a time-varying treatment description applies without additional structural assumptions or a composite or principal-stratum formulation. The paper should acknowledge this limitation and either provide a worked example involving death or temper the general claim that all IEs can be identified with causal-inference constructs.
minor comments (4)
  1. [§4.1] The individual causal effect is written as Y(a=1) – Y(a=1) and later as E[Y(a=1) – Y(a=1)]; these should be Y(a=1) – Y(a=0) and E[Y(a=1) – Y(a=0)]. This appears to be a simple typo, but it occurs in the central definition of the causal effect.
  2. [§5.1.2] The notation E[Y(a=1, D, R)] is nonstandard because D and R are random variables, not fixed values. Consider writing E[Y(a=1)] directly or explicitly defining Y(a, d, r) with fixed d and r and using language such as "with D and R taking their natural values under each assignment."
  3. [§6.1] The claim that the ICH estimand is "likely to be more accessible to clinical trialists without formal causal and mathematical training" is an empirical assertion that is not supported by evidence in the manuscript. Please either cite supporting literature or present this as the authors' opinion rather than an established fact.
  4. [§5.3.2] The phrase "assumptions about the casual relationships" should read "assumptions about the causal relationships."

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper translates ICH estimands into causal estimands via external examples rather than deriving its conclusion from its own definitions.

full rationale

This is a comparative and interpretive paper, not a derivation chain. The central claim that the ICH E9(R1) and causal inference frameworks are complementary is supported by worked examples (PIONEER 1, SYNAPSE, COVID-19) in which each ICH estimand is paired with a causal estimand using standard potential-outcome notation. The causal estimands are constructed to express the same clinical questions as the ICH estimands; this is an explicit translation exercise, not a hidden identity imposed by definition. The paper does not fit parameters and then call them predictions, does not invoke a uniqueness theorem, and does not rely on self-citations to establish its main conclusion. The only self-citations (e.g., Keene 2018 on the arbitrariness of a penalty value, Drury et al. on estimation methods, Keene 2023 on adherence terminology) are background or estimation references and are not load-bearing for the complementarity thesis. The interpretive mapping of intercurrent events to post-baseline mediators or time-varying treatments is presented as an intuitive connection and illustrated with examples; even if it is incomplete for some strategies such as 'while on treatment', that is a scope limitation, not circularity. The paper is self-contained against external benchmarks and its conclusion is an assessment of the exhibited correspondences rather than a restatement of its inputs.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper does not fit any parameters or introduce new entities. It relies on standard causal assumptions from the potential outcomes literature and on the ICH E9(R1) framework as an external standard. No ad hoc axioms are introduced.

assumptions (2)
  • domain assumption Potential outcomes exist for each individual under each treatment, and consistency, positivity, and exchangeability hold.
    Section 4.1 states these as the basic conditions needed to estimate average causal effects from a randomized trial.
  • domain assumption The ICH E9(R1) framework is an authoritative external standard for defining estimands in clinical trials.
    The paper builds its comparison on the five-component definition and the five ICH intercurrent event strategies, citing reference [1].

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Pith. "Pith review of The Estimand Framework and Causal Inference: Complementary not Competing Paradigms." pith.science (2026). https://pith.science/paper/YC54AB4W

@misc{pith2026241212380,
  author       = {Pith},
  title        = {Pith review of: The Estimand Framework and Causal Inference: Complementary not Competing Paradigms},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YC54AB4W}},
  note         = {Machine review of arXiv:2412.12380}
}
read the original abstract

The creation of the ICH E9 (R1) estimands framework has led to more precise specification of the treatment effects of interest in the design and statistical analysis of clinical trials. However, it is unclear how the new framework relates to causal inference, as both approaches appear to define what is being estimated and have a quantity labelled an estimand. Using illustrative examples, we show that both approaches can be used to define a population-based summary of an effect on an outcome for a specified population and highlight the similarities and differences between these approaches. We demonstrate that the ICH E9 (R1) estimand framework offers a descriptive, structured approach that is more accessible to non-mathematicians, facilitating clearer communication of trial objectives and results. We then contrast this with the causal inference framework, which provides a mathematically precise definition of an estimand, and allows the explicit articulation of assumptions through tools such as causal graphs. Despite these differences, the two paradigms should be viewed as complementary rather than competing. The combined use of both approaches enhances the ability to communicate what is being estimated. We encourage those familiar with one framework to appreciate the concepts of the other to strengthen the robustness and clarity of clinical trial design, analysis, and interpretation.

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Reference graph

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