arxiv: 2601.16715 · v2 · submitted 2026-01-23 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Dynamic Expert-Guided Model Averaging for Causal Discovery

Adrick Tench , Thomas Demeester

Authors on Pith no claims yet

Pith reviewed 2026-05-16 11:29 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords causal discoverymodel averagingexpert queryingensemble methodsgraph orientationuncertainty estimationnoisy data

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The pith

Averaging causal discovery algorithms with selective expert queries on disagreed edges improves graph accuracy over standard ensembles.

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

The paper introduces an ensembling technique that combines outputs from several causal discovery algorithms while selectively consulting an imperfect expert on edges where the algorithms disagree most. It separates the questions of edge existence and edge orientation during averaging to draw on the strengths of both data-driven methods and human knowledge. This dynamic querying approach targets uncertainty without requiring full expert access, aiming to produce more reliable causal graphs especially when data violates typical algorithm assumptions. A reader would care because real applications often face noisy or assumption-breaking data where no single algorithm dominates and limited expert input can help without excessive cost.

Core claim

The central claim is that a model averaging procedure which treats edge existence and orientation separately, then queries an imperfect expert only on high-disagreement edges identified across algorithms, yields causal graphs that outperform both individual algorithms and non-expert ensembles on clean and noisy data.

What carries the argument

The disagreement-driven selective querying mechanism that decides which edges receive expert input, while performing separate averaging for edge presence and direction to integrate complementary information sources.

If this is right

The method reduces dependence on any one causal discovery algorithm that may fail under real-world noise or assumption violations.
Expert time is used more efficiently by focusing only on uncertain edges rather than the full graph.
Separate handling of existence and orientation allows algorithms strong at one aspect to complement those strong at the other.
Performance gains persist on both clean and noisy datasets, suggesting robustness for practical deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same disagreement signal could guide expert input in other ensemble settings where uncertainty is hard to quantify directly.
Varying the expert's accuracy level in simulations would show how robust the net-positive assumption remains as imperfection increases.
Extending the approach to use large language models in place of human experts could test scalability without continuous human involvement.

Load-bearing premise

That disagreement among the algorithms reliably marks places where limited input from an imperfect expert will add net value rather than noise to the final graph.

What would settle it

On standard causal discovery benchmarks, compare the final graph accuracy when the expert is queried on disagreement edges versus when those queries are withheld; if accuracy does not rise or falls, the benefit of the dynamic guidance is falsified.

read the original abstract

Would-be practitioners of causal discovery face a dizzying array of algorithms without a clear best choice. This abundance of competitive methods makes ensembling a natural strategy for practical applications. At the same time, real-world use cases frequently violate the assumptions on which common causal discovery algorithms are based, forcing reliance on expert knowledge. Inspired by recent work on dynamically requested expert knowledge and large language models (LLMs) as experts, we present a flexible model averaging method that integrates selective expert querying to ensemble a diverse set of causal discovery algorithms. Crucially, we distinguish between edge existence and orientation, enabling the method to leverage the complementary strengths of data-driven discovery and expert input. We further consider the realistic setting of limited access to an imperfect expert, using disagreement among algorithms to query the expert in cases of greater uncertainty. Experiments demonstrate that our method consistently outperforms strong baselines on both clean and noisy data. Code and data are available at https://anonymous.4open.science/r/expert-cd-ensemble-3282/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A practical ensembling method for causal discovery that queries an imperfect expert on algorithm disagreements and separates edge existence from orientation, but the empirical support for the disagreement proxy needs more scrutiny.

read the letter

This paper introduces a dynamic averaging scheme for causal discovery algorithms that triggers expert queries only where the algorithms disagree on an edge and handles existence and orientation as distinct decisions. The approach is motivated by the reality that no single algorithm dominates and that expert time is limited and imperfect. It tests the idea on both clean and noisy data and releases code, which is a plus for reproducibility. The separation of existence from orientation is a sensible design choice because experts may have different reliability on each. The overall framing is straightforward and targets a genuine applied need. The main soft spot is the empirical foundation. The abstract asserts consistent outperformance over strong baselines, yet the description gives little detail on the exact baselines, metrics, statistical testing, data splits, or noise models. More critically, the central mechanism assumes that disagreement among algorithms is a good proxy for where expert input will help. If the algorithms share systematic biases under misspecification or noise, disagreement could be low on wrong edges or high on correct ones, so selective querying might add little or even hurt. The stress-test note flags this correctly, and without reported correlations, random-query ablations, or per-edge error breakdowns, it is difficult to confirm the proxy holds. This work is aimed at applied causal discovery researchers who have occasional access to domain experts and want a lightweight way to combine off-the-shelf algorithms. A reader looking for a ready hybrid strategy would get practical value from the algorithm description and code. I would send it to peer review. The idea is clear enough and the practical angle is worth referee input, even if the experiments need tightening to address the disagreement assumption.

Referee Report

2 major / 2 minor

Summary. The paper introduces a dynamic expert-guided model averaging approach for causal discovery. It ensembles multiple algorithms, uses disagreement to selectively query an imperfect expert on uncertain edges (distinguishing existence from orientation), and reports consistent outperformance over baselines on both clean and noisy data.

Significance. If the empirical results hold under rigorous controls, the method offers a practical way to combine data-driven causal discovery with limited expert input, addressing real-world violations of algorithm assumptions. The availability of code and data is a positive contribution for reproducibility.

major comments (2)

[Abstract] Abstract: the central claim that the method 'consistently outperforms strong baselines on both clean and noisy data' provides no information on the specific baselines, evaluation metrics, statistical tests, data splits, noise models, or number of runs. Without these details the empirical result cannot be verified and risks post-hoc selection effects.
[Method] Method section (disagreement-driven querying): the assumption that algorithm disagreement is a reliable proxy for true uncertainty (i.e., correlates with ground-truth edge errors) is load-bearing for the selective-querying strategy, yet no correlation analysis, ablation comparing disagreement-based vs. random querying, or per-edge error breakdown is reported to confirm the proxy holds on the tested data.

minor comments (2)

The anonymous code/data link is appropriate for review; upon acceptance it should be replaced with a permanent repository.
[Method] Notation for edge existence versus orientation probabilities should be introduced with explicit definitions early in the method to avoid ambiguity when describing the averaging step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's detailed feedback, which helps improve the clarity and rigor of our work. We respond to the major comments point by point below, and will incorporate the suggested revisions in the updated manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the method 'consistently outperforms strong baselines on both clean and noisy data' provides no information on the specific baselines, evaluation metrics, statistical tests, data splits, noise models, or number of runs. Without these details the empirical result cannot be verified and risks post-hoc selection effects.

Authors: We agree that the abstract would benefit from additional details to better contextualize the empirical claims. In the revised version, we will update the abstract to specify the baselines (individual causal discovery algorithms and their simple average), the primary metrics (Structural Hamming Distance and Structural Intervention Distance), the number of independent runs (20), and note that experiments use both clean data and data with additive Gaussian noise. Full experimental protocols, including data splits and statistical significance testing via paired t-tests, are detailed in Section 4, but the abstract will now provide a concise overview to address verifiability concerns. revision: yes
Referee: [Method] Method section (disagreement-driven querying): the assumption that algorithm disagreement is a reliable proxy for true uncertainty (i.e., correlates with ground-truth edge errors) is load-bearing for the selective-querying strategy, yet no correlation analysis, ablation comparing disagreement-based vs. random querying, or per-edge error breakdown is reported to confirm the proxy holds on the tested data.

Authors: This is a valid point; the selective querying relies on disagreement as a proxy, and we should provide supporting evidence. We will add to the revised manuscript: (1) a correlation analysis showing the relationship between algorithm disagreement on edges and the actual error rate relative to ground truth, (2) an ablation study comparing our disagreement-driven querying to random querying under the same expert query budget, and (3) a breakdown of per-edge improvements. These additions will be included in the Experiments section to empirically validate the proxy assumption on the synthetic benchmarks used. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic proposal validated empirically

full rationale

The paper proposes an algorithmic ensemble method for causal discovery that selectively queries an imperfect expert on edges with high disagreement among base algorithms, distinguishing existence from orientation. No equations, derivations, or fitted parameters are described that could reduce to self-definition or self-citation. Central claims rest on external experimental comparisons to baselines on clean and noisy data, making the work self-contained against independent benchmarks rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on standard causal discovery algorithms plus the domain assumption that disagreement signals useful expert intervention; no free parameters or new entities are introduced in the abstract.

axioms (1)

domain assumption Disagreement among algorithms reliably indicates uncertainty where expert input improves the result
Used to decide selective querying; central to limiting expert access while claiming gains.

pith-pipeline@v0.9.0 · 5468 in / 1158 out tokens · 47178 ms · 2026-05-16T11:29:36.242355+00:00 · methodology

discussion (0)

Forward citations

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