NeurIPS 2026 Papers

Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) to show that assuming that the causal relations are acyclic and invariant across multiple environments (e.g., the way minimum wage affects employment rate is stable across different geographical regions), \textit{only} two auxiliary environments are sufficient to infer the causal graph for arbitrary nonlinear mechanisms. Moreover, we demonstrate that this implies identifiability of the SCM functional mechanisms: as a corollary, we show that \textit{two} auxiliary environments are sufficient to guarantee correct counterfactual inference. We empirically support our theoretical results on synthetic data.

There are many causal discovery algorithms, but few guiding principles for choosing between them. Methods are usually compared through three lenses: identifiability, consistency, and benchmarks. Each introduces assumptions about the data-generating process that are often untestable. We argue that there are no shared standards for comparing, prioritizing, or even discussing those assumptions. We support this claim by showing that (i) identifiability assumptions are often preferred by convention rather than by principle, (ii) uniform consistency requires further untestable assumptions, while also the weaker pointwise consistency has arguably narrowed algorithm design, and (iii) benchmarks are not neutral tests but carry an epistemic role of their own: they quietly fix which regimes matter, which guarantees come into play, and what counts as success. In doing so, they shape what algorithm comparisons are taken to show and thus perform some of the same epistemic work as theory, but less transparently. In this position paper we argue for a more transparent, integrated practice: Make the epistemic standards for discussing identifiability and consistency assumptions explicit, especially their failure modes and scope conditions; design benchmarks to operationalize those standards, by stress-testing and ablating both. Then, theoretical and empirical results jointly clarify when, why, and how algorithms may work rather than accumulate as disconnected advances.

We study heterogeneous treatment-effect (HTE) estimation in observational survival studies commonly associated with both censored outcomes and unmeasured confounding. We integrate causal survival forests (CSF) with negative controls (NC) from proximal causal inference and introduce Negative Control Causal Survival Forests (NC-CSF), a flexible nonparametric HTE learner for survival analysis. In particular, our approach uses a loss that incorporates proxy variables and Neyman orthogonalization to train the random forest, thereby mitigating bias from unobserved confounding and gaining robustness to nuisance estimation. Through extensive simulations spanning varying levels of confounding, proxy relevance, and censoring mechanisms, we demonstrate that NC-CSF substantially reduces bias and estimation error (root mean squared error) relative to existing baselines. We further demonstrate the practical utility of our method on real-world clinical datasets, where it confirms several existing findings and also reveals new interpretable patterns of treatment-effect heterogeneity. To facilitate practical use, we provide an end-to-end Python implementation of NC-CSF that carefully handles implementation details such as nuisance estimation and clipping.

Predictive models trained on observational data often fail to generalise to the distributions they encounter when deployed, especially when the training data is a product of the system being optimised. Recommender systems are a canonical example: they are trained on interaction logs confounded by the deployed policy, past user behaviour, and platform filtering. As a result, the training distribution differs substantially from the candidate distribution scored at serving time, a gap that makes offline metrics unreliable predictors of online performance. We address the distribution shift problem with a method motivated by causal representation learning (CRL). We propose an information-theoretic disentanglement criterion and prove that its optimum depends only on the causal components of the input. We then derive a tractable variational lower bound that makes the criterion optimisable from finite observational data alone. The scope of our method is narrower than that of much of the CRL literature, in that we target better generalisation under distribution shift, not full identification of all latent causal factors. This narrower target is what makes the method practical, requiring only the existing confounded logs, applying to any standard supervised model, and adding no inference-time cost. Our headline evaluation is an A/B test with millions of users on a music streaming platform, applied to a production ranker for personalised playlist generation. A CRL variant matched in capacity to the production baseline performed on par offline but delivered substantial online gains in listener engagement. Complementary evidence on the public KuaiRand recommendation dataset and a synthetic benchmark with known causal structure shows the same pattern: offline parity with baseline, gains under distribution shift. Across all three settings, adding our causal disentanglement objective yields meaningfully better out-of-distribution generalisation.

We study a canonical multi-task demand-learning problem motivated by retail pricing, where a firm seeks to estimate heterogeneous linear price-response functions across multiple decision contexts. Each context is described by rich covariates but exhibits limited price variation, motivating transfer learning across tasks. A central challenge in leveraging cross-task transfer is endogeneity: prices may be arbitrarily correlated with unobserved task-level demand determinants across tasks. We propose a new meta-learning framework that identifies the conditional mean of task-specific causal demand parameters given a subset of task-specific observables despite such confounding, assuming that each task contains at least two distinct locally exogenous price points. This subset is carefully designed to include all of the prices to address cross-task confounding, while masking two demand outcomes that provide randomized supervision to address identifiability issues arising from the inclusion of all prices. We show that this information design is maximally uniformly valid, in that any refinement of the conditioning set that reveals withheld-outcome information is not guaranteed to identify the conditional mean causal target. We validate our method on real and synthetic data, demonstrating improved recovery of demand responses relative to standard transfer-learning baselines.

We study causal inference under outcome interference for sequential, observational settings. We consider settings where the binary outcomes over $N$ units are Markovian across $T$ time steps; at each time step, the outcomes of $N$ units have pairwise dependencies captured through an Ising model; and, each outcome is impacted through a latent external field capturing effects of latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudolikelihood Estimation (MPLE) for learning the model parameters. Under reasonable assumptions, we establish non-asymptotic consistency for parameter estimation. Therefore, sampling from the learnt model enables faithful estimation of causal quantities of interest. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.

It is often easier to destroy the causal content of a training sample than to isolate it. We use this observation to expose shortcut gradients during training. If an intervention removes the semantic evidence that justifies a sample's label while preserving nuisance structure, then any predictive gradient the model still produces on the destroyed view points toward features that can support the label without the causal signal. The destroyed view is constructed from the same sample as the clean view, and the implemented optimizer estimates this shortcut direction on the current minibatch and applies the steering step periodically. Thus CGS injects a recurring within-sample shortcut-correction signal while remaining domain-label-free and close to ERM in cost. We formalize this principle through a nuisance-only Distributionally Robust Optimization geometry, in which an adversary may perturb nuisance factors while holding the semantic component fixed. The resulting local Danskin majorizer induces a first-order robust-feasible cone in parameter space, and Causal Gradient Steering (CGS) is the closed-form projection of the ERM step onto a tractable single-normal approximation of this cone. We further prove target-risk and probe-admissibility guarantees, showing that exact counterfactual generation in pixel space is unnecessary. CGS is compatible with standard first-order training and can act either as a standalone optimizer or as a plug-in correction for existing DG algorithms. It achieves state-of-the-art performance across five standard domain generalization benchmarks. Moreover, using a single locked CGS hyperparameter configuration, it improves seventeen prior DG methods by up to +15.8 percentage points without method-specific retuning, improves OSTrack-256 across GOT-10k, UAV123, and OTB2015, and establishes new matched-protocol state of the art for domain-generalized semantic segmentation by improving SCSD from 51.42 to 52.35 mIoU on GTAV+SYNTHIA$\rightarrow$\{Cityscapes, BDD, Mapillary\} and from 35.66 to 36.99 mIoU on GTAV$\rightarrow$ACDC. Beyond this algorithm, causal signal destruction defines a research direction parallel to data augmentation. The destruction operator is a design axis, and task-specific destroyers can turn shortcut suppression into a reusable tool for broader vision problems.

Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy risks. Standard MIA evaluation requires repeated retraining, which is computationally costly for large models. One-run (single training with randomized data inclusion) and zero-run (post hoc evaluation) methods are often used instead, but their statistical validity remains unclear. We address this gap by framing MIA evaluation as a causal inference problem, defining *memorization as the causal effect of including a data point in the training set*. This novel formulation reveals and formalizes key sources of bias in existing protocols: one-run methods suffer from interference between jointly included points, while zero-run evaluations are additionally confounded by distribution shift between member and non-member evaluation data. We derive causal analogues of standard MIA metrics and propose practical estimators for multi-run, one-run, and zero-run regimes with non-asymptotic consistency guarantees. We validate our approach in several settings, including pretrained and fine-tuned LLMs, showing that it enables reliable measurement of MIA performance without retraining and under distribution shift. Overall, our framework provides a principled foundation for privacy evaluation in modern AI systems.

We study experiments on interacting populations of humans and AI agents, where both unit types and the interaction network remain unobserved. Although causal effects propagate throughout the system, the goal is to estimate effects on humans. Examples include online platforms where human users interact alongside AI-driven accounts. We assume a human–AI prior that gives each unit a probability of being human. While humans cannot be distinguished at the unit level, the prior allows us to compute the average human composition within large subpopulations. We then model outcome dynamics through a causal message passing (CMP) framework and analyze sample-mean outcomes across subpopulations. We show that by constructing subpopulations that vary in expected human composition and treatment exposure, one can consistently recover human-specific causal effects. Our results characterize when distributional knowledge of population composition (without observing unit types or the interaction network) is sufficient for identification. We validate the approach on a simulated human–AI platform driven by behaviorally differentiated LLM agents. Together, these results provide a theoretical and practical framework for experimentation in emerging human–AI systems.

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.

Causal discovery methods produce data-driven hypotheses about causal relationships, which may become new scientific findings or inform decisions about downstream interventions. In such settings, false positive causal claims can be more consequential than false negatives. Yet few methods target false positive error control in causal discovery, and existing error guarantees are often asymptotic and require strong versions of the faithfulness assumption. We introduce the $m$-conservative PC algorithm and prove that it controls false discovery rate in the linear Gaussian setting under an extremely weak version of the faithfulness assumption. In our framework, false positive error for causal graphs is defined based on the conditional-independence information they entail, yielding a metric that penalizes both false adjacencies and incorrect edge orientations. The parameter $m$ quantifies a trade-off between algorithmic complexity and the strength of the required faithfulness assumption. The $m$-conservative PC algorithm combines undirected graph learning, a Benjamini-Yekutieli-style procedure for conditional independence testing, and a conservative orientation step that marks unresolved or contradictory edge directions as ambiguous. Our algorithm produces valid outputs and retains error guarantees even in the finite-sample setting, despite possible random errors in the conditional independence tests.

We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank functional decomposition. LRQS subsumes location-scale noise models and post-nonlinear heteroscedastic noise models, while allowing multiple quantile bases to represent changes beyond location-scale effects. We prove generic identifiability of LRQS: the transformed quantile surface is low rank in the causal direction, whereas reverse representability under the corresponding constraints occurs only for exceptional, fine-tuned cause marginals. We provide a simple-yet-powerful causal score using a nonparametric fitting procedure that alternates between rank-constrained approximation of discretized quantile surfaces and isotonic estimation of the unknown monotone transformation. Experiments on synthetic mechanisms with higher-rank distributional shape variation and strong nonlinear distortions, together with standard bivariate benchmarks, show that LRQS is especially effective when conditional distributional shape or observation distortion goes beyond existing location-scale assumptions.

Causal discovery aims to uncover the causal relationships among variables beyond correlations from observational data. On time series, a major challenge is nonstationarity, which is typically modeled as regime-based shifts or drifting causal strength. In contrast, we consider non-stationarity arising from varying cause-to-effect delay, and introduce a causal model that is identifiable with few assumptions. To discover causal relationships with varying causal delay in practice, we formalize the problem in terms of the algorithmic model of causation, and propose STRETCH to discover causal graphs. We show on synthetic data that our method correctly recovers the causal relationships, and show the relevance of this causal model on a case study on the impact of El Niño climatic phenomenon on Indian monsoon.

Existing causal discovery methods for interventional data typically follow one of two paradigms: they either require explicit environment labels and known intervention targets, or, in the absence of such metadata, they rely on computationally intensive procedures such as first inferring intervention targets or employing complex models to account for the latent variables of environment labels. Surprisingly, we show that neither explicit environment labels nor auxiliary inference algorithms are strictly necessary: under certain conditions, the classical ICA-LiNGAM algorithm consistently recovers the causal graph from simply pooled interventional and observational data. We first demonstrate that in linear Gaussian structural equation models where interventions independently perturb the noise distributions across nodes, pooling produces exactly independent, non-Gaussian sources, the appropriate setting for Independent Component Analysis (ICA). Because the assumption of strictly independent interventions can be overly restrictive, we extend our analysis to the practical setting of single-node (atomic) interventions. While pooling atomic interventions induces the necessary non-Gaussianity, it also introduces source *dependence*, violating a core ICA assumption. However, theoretical analysis establishes that this dependence decays as $\mathcal{O}(1/n^2)$ with the number of variables $n$, whereas non-Gaussianity decays only as $\mathcal{O}(1/n)$. This yields a regime of mild misspecification at moderate $n$ where sources are near-independent yet sufficiently non-Gaussian for ICA to succeed. In synthetic experiments, where data is pooled across many different environments, our label-free approach matches or exceeds label-aware methods. On real-world data, our approach performs comparably to methods that explicitly require environment labels. Ultimately, our work challenges the assumed necessity of environment labels and complex inference mechanisms in interventional causal discovery and suggests a re-evaluation of the often-overlooked classical ICA-LiNGAM algorithm.

Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not representative of the underlying populations. Due to its practical importance, related works have been proposed to address the problem under local [Versteeg et al., 2022] or interventional settings [Dai et al., 2025a], but general algorithms for observational data remain elusive. In this paper, we focus on truncated data (hard selection), and provide a general result for Additive Noise Models (ANM). We show that score-based methods are a surprisingly suitable candidate due to a special property of the score function ($\nabla \log p(x)$): *deterministic truncation preserves both the score* of the joint distribution as well as the Jacobian of the score. While established score-matching algorithms still fail in most settings, we actively leverage this observation to propose a new method for causal discovery that is robust under truncated data with ANMs. Overall, our approach offers a general, robust solution that is agnostic to external information about the selection process and still achieves comparable performance to the state-of-the-art approaches when selection is not present.

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.

This position paper argues that causal benchmarks for multimodal AI should measure categorical difference, not capability gap. The field reports machine causal performance against human performance and interprets progress as gap-closing along a shared scale. The shared-scale assumption rests on a flattened reading of Hume's regularity theory in which causation reduces to constant conjunction across instances and the temporal experience of the observer has no role. When understood in terms of its presuppositions, Hume's regularity is inseparable from a temporally extended subject who undergoes the conjunctions over time. Human and machine causal cognition therefore have different temporal architectures. The two are grounded in time as substrate and time as dimension, a categorical difference that scale or additional intervention machinery cannot close. Evaluation paradigms that treat the two as commensurable produce inflated assessments of machine causal competence that independently scored probes cannot capture. We propose \textit{Anthropomorphic Competence Inflation} as a construct the field can develop into a broader measurement instrument and formalize it through the \textit{Inflation Score}. Preliminary observations on two recent vision-language models match the pattern predicted by the categorical claim. The implications affect evaluation practice, deployment in causal-decision domains, and the next phase of AI causality research.

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.

Cauchy activations and Cauchy-adaptive layers can give large gains on scientific learning tasks, but the gains are selective and not uniquely Cauchy-specific. We organize this selectivity with an empirical loss-architecture alignment taxonomy: an architecture can help when the objective exposes structures it can use, and can lose under a mismatched objective. In score learning, Cauchy score networks improve over SiLU under a Fokker-Planck PDE loss, but the same advantage disappears under denoising score matching and a Gaussian activation is comparable to Cauchy under the PDE loss. In loss-activation factorials, robust residual losses explain more of the heavy-tailed and spectral gains than the activation alone; an Allen-Cahn trap initially escaped by CauchyAct+Cauchy loss is also escaped, sometimes more strongly, by CauchyAct+L1, CauchyAct+Huber, and ReLU+Cauchy loss. We also analyze ResidualCAN, a CauchyAct main path plus a softmax-weighted Cauchy Adaptive Node residual. It achieves 1520x lower stationary Allen-Cahn error than a SiLU MLP at epsilon = 1.0 and remains strongest or second strongest against KAN-style, tuned SIREN/Fourier, and adaptive PINN baselines across an Allen-Cahn epsilon-suite. Ablations show that most of this gain comes from a dense local-basis residual rather than from Cauchy activation alone. A Poisson/Burgers boundary check prevents a broad PDE dominance claim: tuned SIREN/Fourier and KAN-style baselines can dominate away from transition-layer structure. The resulting claim is deliberately bounded: Cauchy components are one useful instance of robust/local-basis co-design in low-dimensional scientific tasks, not a universal activation or a uniquely rational mechanism.