Title: Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
Venue: NeurIPS 2025
Accepted: yes
Keywords: Shapley

Explainability

Causality

Attribution

SCM

Modeling
Abstract: Among explainability techniques, SHAP stands out as one of the most popular, but often overlooks the causal structure of the problem. In response, do-SHAP employs interventional queries, but its reliance on estimands hinders its practical application. To address this problem, we propose the use of estimand-agnostic approaches, which allow for the estimation of any identifiable query from a single model, making do-SHAP feasible on complex graphs. We also develop a novel algorithm to significantly accelerate its computation at a negligible cost, as well as a method to explain inaccessible Data Generating Processes. We demonstrate the estimation and computational performance of our approach, and validate it on two real-world datasets, highlighting its potential in obtaining reliable explanations.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nDo-Shapley values are expensive to estimate since they require evaluating exponentially many causal queries. The authors propose to make their estimation more feasible by employing two techniques in the context of do-Shapley values:\n1) They propose to employ \"estimand-agnostic (EA)\" causal inference which -- instead of fitting a different model for every query (EB) -- fits a Structural Causal Model (SCM) once (offline) which encodes all possible interventional distributions, and which can then be used to answer the causal queries.\n2) They propose to skip causal queries which are known to evaluate to zero since the intervention is causally independent of the target given the interventions that were already implemented before.\nFurthermore, the authors touch on discussing whether do-Shapley values should be based on counterfactual (rung 3) instead of interventional (rung 2) causal queries, and how they can be defined depending on whether the prediction or the underlying target is explained.\nIn Experiments, the authors evaluate how the choice of SCM (linear vs nonlinear) affects the quality of the effect estimation in a small synthetic setting, demonstrate that do-SHAP is different from marginal SHAP, and show that contribution 2) allows speeding up the estimation of do-SHAP in small synthetic settings.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n*Significance:* I am convinced that in practice many causal queries can be skipped given knowledge of the causal graph, and that this can lead to a significant speedup in estimation. As such, the main idea of the paper has practical significance.\n\n**Weaknesses**\n\n*Clarity:* The paper touches on several distinct problems, and I found it challenging to understand which of them the authors actually intend to tackle:\n\n- A Accurately estimating (many) causal queries from data. (EB vs EA)\n- B Identifying queries that are redundant. (FWR)\n- C Discussing whether do-SHAP are the \"right\" explanation method. (e.g. section 4.3)\n\nIn my view B is the main contribution, and I would recommend the authors to focus on clearly explaining that. \n\nIn particular, I was confused about the role of EB vs EA for the paper: The EA approach is prominently discussed in the introduction and conclusion, and the EA approach introduced in great length in the background. But in the main paper EA only receives very little attention (a bit at the beginning of 4.1) -- there is no methodological or empirical contribution.\n\nIn the main section of the paper, Section 4, there are three small subsections: 1 \"Do-Shapley value\", 2 \"Efficient estimation of the do-Shapley values\", 3 \"do-Shapley explanations\". 1 could be part of the background, and 3 should be left out or should be discussed together with 1. The really interesting part 2 is very dense and relevant details are missing (for example, what does it mean to $\\Phi$-encode nodes?)\n\n*Originality:* The paper employs existing ideas in a somewhat novel context. Luther et al (2023) already showed that Shapley approximation can be significantly accelerated using knowledge of the causal graph, the difference is that Luther et al make use of statistical independence to speed up conditional SHAP, while the reviewed paper uses causal independence to speed up do-SHAP.\nI don't see relevant contributions in the context of EA vs EB.\n\n*Quality:* The theory is sound. However, the proposed algorithm is quite complex given how simple the solution could be: Shapley values evaluate v(S u j) - v(S) for many sets S. Why not simply check causal independence of j and Y given S for every evaluation (very easy using a causal graph) and skip the evaluation if there is a causal independence in the spirit of Luther et al (2023)? What is the benefit of first identifying all possible irreducible frontiers first?\n\nQUESTIONS:\nYou claim that the approach makes the estimation of do-Shapley feasible for \"arbitrarily complex graphs\". Where do you substantiate this claim?\nYou continue by saying that you achieve this \"by using the estimand-agnostic approach\". Where do you show that EA is responsible for the claimed performance improvements?\n\nLIMITATIONS:\nLimitations and social impact are discussed appropriately.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript proposes a practical method for computing do-Shapley values (do-SVs) — causal, interventional variants of Shapley value feature attributions — using an estimand-agnostic (EA) approach. The authors replace the original estimand-based (EB) procedure from prior work with an EA technique, which involves fitting a single structural causal model (SCM) to the data and using this proxy model to estimate any identifiable causal query. The paper introduces a novel Frontier-Reducibility Algorithm (FRA) that exploits graph structure to accelerate do-SV computation by identifying coalitions whose interventional effects can be safely skipped. Experiments on real and simulated data suggest that FRA can lead to major speedups in practice.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n*Problem significance*: Extending Shapley values to causal settings is important for fair, trustworthy, and scientifically grounded explainability in ML. \n\n*Originality*: As far as I'm aware, the proposed EA framework for computing do-SVs is novel, contributing to both the explainability and causal inference literatures.\n\n*Technical contribution*: The FRA algorithm is a well-motivated and technically interesting technique to exploit graph structure for computational gains. Though somewhat similar to prior work (Luther et al., 2023), the similarities and differences are properly acknowledged in the manuscript.\n\n*Empirical validation*: The experiments convincingly demonstrate that the method works on synthetic and real-world data.\n\n*Clear exposition*: The paper is generally well-written, with clear descriptions of causal concepts, algorithm design, and experimental setup. I especially appreciated the thorough appendix.\n\n**Weaknesses**\n\n*Confusing and potentially misleading claims about tractability*: Throughout the paper, the authors repeatedly imply that their method renders do-Shapley value computation practical on arbitrarily complex graphs (e.g., abstract lines 4–7; intro, final paragraph; top of Sect 4; conclusion, first paragraph). However, the method does not in fact circumvent the fundamental #P-hardness of Shapley value computation, as acknowledged in Sect. 5.2. The FRA algorithm accelerates the process in some cases but does not resolve the exponential scaling in the number of features; more generally, the speedups it offers appear to be a function of graph sparsity, although the precise nature of this relationship is never spelled out in detail. This is a missed opportunity, as there has been a number of interesting works on the complexity of Shapley value computation in the last few years (Van den Broeck et al., 2022; Arenas et al., 2023; Marzouk et al., 2025). This tension between claimed practicality and residual intractability needs to be clarified.\n\n*Limited discussion of identifiability limitations*: The method assumes all required causal queries are identifiable from the causal graph, but the paper downplays the practical difficulty of ensuring this in complex or partially observed systems.\n\n*No quantitative comparison to baseline SHAP runtimes*: The paper lacks a direct empirical comparison of total runtime or computational cost between EB do-SHAP, conditional/marginal SHAP, and the proposed EA do-SHAP+FRA method. (Unless I'm misreading Fig. 4c?)\n\n**References**\n\n-https://www.jair.org/index.php/jair/article/view/13283/26815\n\n-https://jmlr.org/papers/v24/21-0389.html\n\n-https://arxiv.org/abs/2502.12295\n\nQUESTIONS:\nFull disclosure: I have reviewed a previous version of this manuscript. I think it is much improved, and am generally disposed to recommend acceptance. I have just one major question/comment, and a few minor ones.\n\n**Major comment**\n\n*Clarifying tractability*: As noted above, there is some confusing language around the precise advantages conferred by the FRA approach. Experiments appear to confirm that this often speeds things up in practice, as we would expect. But the word \"efficient\" has a precise meaning in computer science – or maybe I'm just old fashioned :) – and it is clear from the discussion in Sect. 5 that the proposed method is not, in fact, a polytime algorithm. Now it may turn out to be polytime under some graph sparsity assumptions, and it would be very cool to spell this out in a bit more detail. But in lieu of such an analysis, I recommend striking all mention of \"efficient\" algorithms for arbitrary graphs, etc. \n\n\n**Minor comments/questions**\n\n-Since EA methods rely on a known, accurate causal graph, how sensitive are the estimated do-SVs to graph errors?\n\n-Linearity is usually included at a Shapley value axiom (Appx. A).\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe Shapley value is a well-known concept from game theory. We are given a set of players X and a valuation function v which assigns each subset S (\"coalition\") of X a value v(S). The Shapley value of a variable P is the average value that adds P to every coalition S, that is, v(S \\cup X) - v(S). \n\nThe Shapley-value is now used to explain the influence of certain observed samples x on a target variable Y. To do so, the valuation function is set to E[Y | do(S = s)] for every subset S of X. Here s is the restriction of x to S. This assumes that there is an underlying SCM whose structure as a graph is known s.t. the expected values are all identifiable (using do-calculus). \n\nTo turn this into a practical scheme, the authors present an estimand-agnostic approach. To compute the Shapley value, one needs to estimate an exponential number of terms. The key is to just learn the distribution of a single SCM, then one can check whether v(S) is identifiable using do-calculus and then estimate v(S) using the learned distribution and SCM-calculation rules.\n\nThe authors present a way to calculate the do-Shapley value faster by the following observation. If X is an ancestor of Y and the coalition we want to estimate contains a blocking set between X and Y, then adding X to the coalition does not change the value of the coalition. This allows one to do a faster than brute-force computation, the authors design a nice frontier reduction algorithm to perform this computation fast.\n\nThe author also provide an implementation and do extensive testing.\n\nSTRENGTHS AND WEAKNESSES:\nI think this is a nice paper. I combines nice theoretical insights with a practical implementation. The proofs presented in Appendix D are very detailed and well-written.  I am mainly working on ML theory, therefore I found it hard to evaluate the partical part. Therefore, my confidence is only 3. My reservations only stem from the fact that I am not able to confidently evaluate the practical part. \n\n\nComments:\n\n- line 91: The permutation only induces an ordering if S is ordered\n- line 152: SCM G -> graph G (?)\n- line 186: Assumption 4.1 needs to be reworded in my opinion\n- line 205: Should Y also not be in the blocking set\n- line 210: An irreducible frontier would be called minimal cut in graph theory\n- line 219: I find it easier to write that you you encode the set by its characteristic vector stored in an integer. Also the word $\\phi$-encodes is not defined.\n- line 223: That being said, I would present the set-version (in the appendix) of the algorithm as Algorithm 1. While of course the presented version runs faster, the set-version is much easier to understand.\n\nQUESTIONS:\n- Are there any known formal hardness results for computing Shapley values in this setting? Shapley value per se is an exponential sum, but depending on the valuation function, it can be done faster. \n- Can one get estimates in Appendix B when also the underlying SCM is taken into account?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors claim the following contributions:\n1. The work constructs an neural causal model-style approach to estimate do-Shapley-values, assuming identifiability.\n2. The work also limits the exponential number of terms computed requusing their Frontier-Reduction Algorithm (FRA) to generate irreducible subsets.\n\nSTRENGTHS AND WEAKNESSES:\nOriginality: The idea of training a proxy model to estimate do-Shapley values specifically is only marginally novel, given that it comes from the neural causal model framework. The concept of the frontier reducibility algorithm seems logical, stemming from d-separation results, and it primarily seems to be an algorithm for empirically speeding up do-SV estimation.\n\nSignificance & quality: The contribution seems primarily empirical and are claims are the made on the basis of experiments on synthetic data. There does not seem to be a performance or runtime comparison between estimand-based approaches to do-Shapley estimation and the described neural causal model-style approach, so it is challenging to see an improvement from baseline. In cases where not all variables have direct effects on Y, FRA is shown to boost estimation efficiency.\n\nClarity: The work is clearly written.\n\nQUESTIONS:\n1. Is there a performance or runtime improvement over (Jung et al., 2022)'s estimand based approach when not using FRA?\n2. Is it correct to say that FRA does not yield runtime improvements when all variables X affect Y directly (e.g., when $|Pa_Y| = |\\mathbf X|$ or $p = 1$)?\n3. How significant is the improvement with respect to the proportion of variables that do affect Y directly (e.g., how does the improvement change as $p$ goes to 1, given it is currently set to 0.25)?\n4. Does the FRA algorithm work with semi-Markovian causal diagrams?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}