NeurIPS 2026 论文列表

Latent confounding poses a significant obstacle to identifying the causal effect of a treatment on an outcome. To address this, many existing studies leverage proxies of the latent confounder to indirectly adjust for the confounding bias. However, they impose specific structural constraints on the proxies and typically require multiple proxies. In this paper, focusing on the latent variable linear non-Gaussian acyclic model (lvLiNGAM), we propose a causal effect identification procedure requiring only a single agnostic proxy. Crucially, the term "agnostic" means that the causal connections between the proxy and the treatment-outcome pair can be arbitrary and are not required to be known a priori. This structural complexity precludes identifying the causal effect via a simple closed-form formula. Consequently, our identification procedure is designed to first derive candidate solutions from cumulants and then isolate the valid solution by examining certain independence relationships. We present a series of new theoretical results, which collectively establish the soundness of our identification procedure: given the observational population distribution, it correctly identifies the true causal effect when identifiable, and correctly reports unidentifiability otherwise. Finally, we empirically validate our theoretical results.

Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demographic disparities that exist in the world. In this context, causal inference provides a principled framework for reasoning about fairness, as it links observed disparities to underlying mechanisms and aligns naturally with human intuition and legal notions of discrimination. Prior work on causal fairness primarily focuses on the standard machine learning setting, where a decision-maker constructs a single predictive mechanism $f_{\widehat Y}$ for an outcome variable $Y$, while inheriting the causal mechanisms of all other covariates from the real world. The generative AI setting, however, is markedly more complex: generative models can sample from arbitrary conditionals over any set of variables, implicitly constructing their own beliefs about all causal mechanisms rather than learning a single predictive function. This fundamental difference requires new developments in causal fairness methodology. We formalize the problem of causal fairness in generative AI and unify it with the standard ML setting under a common theoretical framework. We then derive new causal decomposition results that enable granular quantification of fairness impacts along both (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms. We establish identification conditions and introduce efficient estimators for causal quantities of interest, and demonstrate the value of our methodology by analyzing race and gender bias in large language models across different datasets.

We introduce a categorical framework for causal abstraction that unifies and extends existing approaches. Modeling causal systems as Markov functors from free Markov categories generated by directed acyclic graphs, we define a causal abstraction as a deterministic natural transformation together with an embedding of restricted free Markov categories. This separates graphical compatibility under interventions from domain-level clustering of variables or values. Our framework provides an explicit characterization of admissible high-level graphs and recovers prior notions such as constructive -abstractions and cluster-DAG abstractions with unobserved confounders. We give concise categorical proofs that interventional distributions factorize over graphical abstractions and that do-calculus applied to the high-level graph yields valid conclusions for the low-level model.

Many decision-facing stochastic systems are observed through aggregate distributions rather than scalar trajectories: queue occupancies, mobility shares, public-health mixtures, generation-source shares, ecological compositions, and air-quality severity profiles all live on the probability simplex and evolve over time. We study causal (time-respecting online) forecasting for these distribution-valued time series and argue that the transition operator itself should be structured around the simplex. We introduce CAST (Causal Anchored Simplex Transport), a successor-local operator that (i) retrieves empirical successors from causal context, (ii) stabilizes them with a persistence anchor, and (iii) applies a bounded local stochastic transport on ordered supports; every stage preserves the simplex by construction. We identify a structural failure mode, latent transition-kernel aliasing, where similar observed distributions evolve differently under different contextual regimes, and prove that any forecaster depending only on an aliased summary incurs an irreducible weighted Jensen--Shannon excess-risk lower bound, while the CAST hypothesis class contains the regime-aware Bayes successor; for ordered supports an additional Pinsker separation holds whenever the transported successor lies outside the no-transport anchor hull. On a suite of eleven public and simulated benchmarks spanning ecology, energy, diet, mortality, employment, air quality, severe weather, mobility, and $G/G/1$, $G_t/G/1$ queue occupancy, CAST achieves the best average rank on both one-step KL (1.27) and autoregressive rollout JSD (1.91), winning 8/11 sections on each metric against a broad statistical, compositional, recurrent, convolutional, Transformer, and modern time-series baseline set, and top-2 on all 11 sections for offline KL. Component ablations and a controlled synthetic aliasing experiment corroborate the theory.

Robust visual recognition under degraded imaging conditions is critical for real-world vision systems.Task-oriented image restoration addresses this problem by enhancing degraded inputs before recognition, but it must preserve natural visual appearance without disrupting the evidence used by downstream recognizers.Existing optimization strategies struggle to satisfy these requirements simultaneously.Restoration driven mainly by visual quality may weaken task-critical cues or introduce texture shifts that are visually plausible but poorly matched to the recognizer.Directly tuning restoration networks with recognition loss is also insufficient, as the restoration model may exploit recognizer-specific shortcut cues and produce visually unnatural artifacts while improving task scores.We propose Causal Mediated Unrolling (CaMeU), an optimization-based framework for task-oriented image restoration with frozen recognition models.Starting from a joint optimization objective, the proposed framework derives a Half-Quadratic Splitting formulation that separates restoration into alternating task-driven and prior-guided updates.The task-driven update introduces an explicit mediator for front-door guided optimization, reducing shortcut bias induced by the recognizer.The prior-guided update performs manifold projection, pulling the intermediate result back toward the natural-image manifold to preserve visual quality.Through this mediated alternating process, the restored image is progressively optimized for both recognition performance and visual quality.Experiments on VOC Haze, VOC Dark, and degraded CUB-200-2011 show that CaMeU consistently improves downstream recognition while reducing visual artifacts.

Bayesian causal inference quantifies uncertainty in treatment effects under a specified causal model. This uncertainty is often interpreted as evidence that the resulting causal conclusion is robust. We argue these are distinct targets: a posterior over a treatment effect may be tightly concentrated above zero while the corresponding causal claim is fragile to small violations of ignorability, overlap, or target-population assumptions. We introduce Bayesian causal stress testing (BCS), a framework that assigns a posterior fragility score $F_\alpha$ with a threshold $\alpha\in(0,1)$ to a causal conclusion by measuring the smallest calibrated stress under which the posterior probability of the claim falls below $\alpha$. We give a stress-map formulation, decision and composition results for claim-level stress, a calibration theorem showing that $F_{0.5}$ asymptotically recovers the partial-correlation form of the Cinelli-Hazlett robustness value in the linear-Gaussian flat-prior limit, and a proposition formalizing why posterior precision and posterior fragility can decouple. We evaluate BCS on IHDP, TWINS, and ACIC-cov+Hill, a semi-synthetic benchmark built from the ACIC 2016 covariate matrix, under BART, BCF-style, Bayesian linear, and Bayesian-bootstrap AIPW posteriors. On the linear Bayesian posterior, $F_{0.5}^2$ matches the Cinelli-Hazlett $RV_{0.5}^{\mathrm{CH}}$ to RMS $0.001$ on both IHDP and ACIC-cov+Hill across all $20$ configurations; the BART and BCF-style posteriors show larger deviations in small-effect configurations. Across estimators, posterior $95$% interval widths vary by up to $50$% but $F_{0.5}$ remains within $0.05$, illustrating that posterior precision is not a robustness certificate. A controlled-DGP calibration experiment with a hidden confounder of known partial correlation $\gamma_{\mathrm{true}}$ confirms that $F_{0.5}$ tracks the required flip-stress to mean absolute error $0.03$.

Causal inference is central to scientific discovery, yet choosing appropriate methods remains challenging due to the complexity of statistical methodology and real-world data. Inspired by the success of artificial intelligence in accelerating scientific software, we introduce an evolutionary framework that uses large language models to discover and iteratively refine causal methods. Across benchmarks, our estimators consistently outperform established baselines: our best estimator lay on the Pareto frontier of 58 human submissions for a recent community competition. We also extend the algorithm to achieve competitive results in settings with estimated rewards. Analysis of the evolutionary trajectories shows that agents progressively discover sophisticated strategies tailored to unrevealed data-generating mechanisms. Our findings suggest that language-model-guided evolution could be used in scientific settings with partially observed rewards such as causal inference.

Aphasias, selective language impairments which can arise from brain damage, reveal the functional organization of human language by providing causal links between affected brain regions and specific symptom profiles. Drawing on this literature, we introduce an aphasia-inspired technique to characterize the emergent functional organization of language models (LMs). We ``lesion'' (zero-out) model parameters and measure the effects of this intervention against clinical aphasia symptoms, as diagnosed by the Text Aphasia Battery (TAB). When applied to 112,426 outputs from five 1B-scale LMs, the full range of evaluated symptoms surface, but in distributions largely distinct from those of humans. Our method uncovers broad, component-level symptom differences between attention (query, key, value, output) and feed-forward (up, gate, down) projections, without any significant within-class differences. We also find an effect of depth, where lesions in early layers disproportionately cause syntactic and semantic symptoms while late-middle layers yield higher rates of phonological and fluency deficits. Although some LM lesions induce quantitatively more similar profiles to some human aphasia types than others, qualitative differences in symptom patterns between LMs and humans suggest that aphasia syndromes are heavily influenced by the details of learning and processing rather than being a domain-invariant consequence of disrupted language processing.

Sequential recommendation is a central task in recommender systems, and recent research has increasingly shifted toward generative recommenders that leverage both sequential patterns and semantic item information. However, these methods are often evaluated on a small set of widely used benchmarks. This raises a natural question: do these benchmarks actually require the advanced modeling capabilities of modern generative recommenders? We conduct a benchmark audit using an intentionally simple graph heuristic: starting from only the most recent one or two interacted items, it retrieves candidates from a few-hop item-transition graph and ranks them with item-feature similarity. Surprisingly, despite its simplicity, this heuristic matches or outperforms a broad set of modern baselines on a variety of popular sequential recommendation benchmarks. For example, it achieves relative NDCG@10 improvements of 38.10\% and 44.18\% over the best competing baseline on the widely used Amazon Review Sports and CDs datasets, respectively. We further show that this phenomenon is not merely an artifact of a particular heuristic, but reflects shortcut solvability in existing benchmarks. Specifically, we identify three potential shortcut structures that could make next-item prediction easier than expected: low-branching local transition structure, feature-smooth transitions, and limited dependence on long user histories. These shortcuts do not need to appear simultaneously. Depending on the dataset, even one or two strong shortcut signals can make simple local retrieval highly competitive, while weakening the relevant signals allows more sophisticated models to show clearer benefits. Our broader evaluation across 14 diverse datasets further shows that model rankings change substantially with dataset properties, while the simple graph heuristic remains competitive on 10 out of 14 datasets. These findings suggest that strong performance on several standard sequential recommendation benchmarks may not faithfully reflect whether recent methods achieve the advanced modeling capabilities they aim to demonstrate. Rather than treating datasets as interchangeable leaderboards, we argue for more careful dataset selection and dataset-level diagnostic analysis when using benchmarks to support claims about the benefits of new recommendation models.

Recommendation unlearning (RU) aims to remove the influence of specified user--item interactions from a trained recommender while preserving utility on the remaining data. Existing RU methods usually require access to the original training data, especially the remaining data, to recover recommendation utility after unlearning. However, access to such data is often restricted or unavailable in practice due to privacy constraints, storage costs, or third-party deployment, giving rise to source-free recommendation unlearning. To address this challenge, we propose an adaptive source-free recommendation unlearning (**AdaSRU**) method that enables effective unlearning without accessing the original remaining data. **AdaSRU** first estimates the inaccessible gradient on the remaining data. It exploits the first-order optimality condition of the trained recommender and approximates the remaining gradient using the forgetting data and squared gradient accumulators from adaptive optimizers, e.g., Adam. **AdaSRU** then formulates unlearning as a gradient-constrained update, where the estimated remaining gradient is used as a constraint to limit utility degradation while maximizing the unlearning objective. **AdaSRU** adaptively adjusts the utility-preserving correction according to the current gradient geometry. We further theoretically prove that **AdaSRU** approaches an approximate Pareto stationary solution. Extensive experiments on three real-world datasets demonstrate that **AdaSRU** achieves strong performance in both unlearning and recommendation. The code is available at https://anonymous.4open.science/r/AdaSRU-94D9/.

While recent multimodal recommender systems have demonstrated the effectiveness of incorporating visual and textual information to improve downstream performance, most existing methods rely on static modality fusion, assuming that the relative importance of textual and visual signals remains stable across recommendation scenarios. This design may not fully account for an important variation across recommendation requests: some queries require fine-grained visual cues, whereas others are better served by textual or functional semantics, in which case indiscriminate modality fusion brings in uninformative cues and impairs recommendation quality. To address this, we propose AdaM-Rec, an LLM-based framework for adaptive modality routing in multimodal recommendation, which enables dynamic calibration of reliance on textual and multimodal evidence for user-specific queries. Built on structured natural-language representations of items and user preferences, it estimates modality reliability using proxy recall tasks. Specifically, it generates pseudo-queries that match the granularity of the actual query while pointing to the user's positively interacted items as verifiable proxy targets, evaluating which modality yields better recall performance in analogous scenarios and optimizing the routing strategy in an agentic manner. It then performs routed recall with optimized strategy, enriches results with collaborative items, and ranks candidates by their relevance to both the query and user preferences. Experiments demonstrate that AdaM-Rec delivers strong performance against state-of-the-art baselines, highlighting the effectiveness and broader potential of adaptive control over modality reliance in multimodal recommendation. Code will be publicly available.

Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely answer a prompt. A useful evaluation must let agents choose experiments, interpret finite evidence, and make causal decisions, while keeping the environment coherent across repeated observations and interventions and preserving objective ground truth. We introduce ACED-BENCH (Active Causal Evidence and Decision Benchmark), a neuro-symbolic benchmark built around this requirement. The key idea is to separate the readable scientific surface from the formal causal world: LLMs generate natural-language scenarios and task text, while hidden probabilistic graphical models define the causal graph, data-generating process, intervention semantics, validation checks, and oracle answers. This yields scientific worlds that are natural for agents to investigate but exact enough for scalable, programmatic evaluation. The main leaderboard ACED-CORE contains 180 tasks: 60 set-valued causal ancestry/descendancy questions and 120 constrained causal-decision tasks. We also report ACED-QUAL, a 150-question yes/no qualification check using the same hidden-PGM interface. We evaluate seven contemporary LLMs and two earlier-generation models across three active agent designs. ACED-QUAL checks whether agents can use the interface to draw conclusions from evidence, while ACED-CORE tests whether they can decide what evidence to collect and when to commit to an answer. Evidence-ledger analysis localizes failures within the investigation: agents test the wrong intervention, lose relevant evidence, or continue after sufficient evidence has appeared. These results suggest that reliable AI co-scientists need explicit support for experiment design, evidence retention, answer commitment, and stopping.

Linear structural causal models (SCMs) are used to analyze relationships among random variables. In these models, directed edges encode direct causal effects, while bidirected edges capture latent confounding. The problem of generically identifying the hidden parameters from observed correlations remains open in causal inference. The half-trek criterion by Foygel, Draisma, and Drton [2012] and its generalization to the edge-wise criterion are important criteria for generic identification in linear SCMs, since the half-trek criterion can be efficiently decided by an algorithm with running time $\mathcal{O}(n^5)$. We here develop a new criterion that is equivalent to the edgewise criterion. Utilizing this criterion, we design a faster randomized algorithm for deciding the half-trek criterion with running time $\mathcal{O}(n^4)$. For sparse graphs, our algorithm even works in $\mathcal{O}(n^3)$. We show that the algorithm is useful in practice by providing an implementation that outperforms the implementation of HTC in the state-of-the art R package SEMID.

Causal reasoning from observational data becomes significantly challenging when the underlying causal structure is only partially known. In this work, we study the the equivalence class of structural causal models (SCMs) with latent variables sharing the same set of conditional independencies, as represented by a partial ancestral graph (PAG). Specifically, we provide a new characterization of SCM-induced causal diagrams in this equivalence class using differentiable parameters. Building on this characterization, we introduce a new model class called PAG-constrained neural causal models ($\mathcal{P}$-NCMs) which parameterize this equivalence class of SCMs. We prove that $\mathcal{P}$-NCMs are expressive enough to represent counterfactual distributions induced by any SCM in the class, while remaining consistent with the structural constraints shared across all its members. Finally, we demonstrate how the differentiable parameterization of this model class enables causal inference under Markov equivalence by reducing counterfactual partial identification to an optimization problem over $\mathcal{P}$-NCMs. We establish the theoretical soundness of this approach and validate its performance on simulations.