NeurIPS 2026 Papers

Large language models (LLMs) generally lack deep causal reasoning capabilities in temporal reasoning. Inspired by document layout reordering, this paper proposes a paradigm shift from discrete narratives to causal topologies. It constructs two architectures: a training-free neuro-symbolic framework, temporal causal reasoning agent (TCR-Agent), which achieves interpretable causal deduction and counterfactual truncation through external causal graph instantiation and a symbolic intervention engine; and an end-to-end model, temporal causal reasoning former (TCR-Former), which internalizes these mechanisms into the Transformer attention space via a causal-topological attention mask and temporal span biases. Experiments on the TempoCausal benchmark demonstrate that TCR-Agent comprehensively surpasses existing training-free baselines. At the same time, TCR-Former achieves an accuracy 16.5% higher than that of state-of-the-art (SOTA) methods, outperforming large-scale models such as GPT-5.4 and DeepSeek-V3.2 with only approximately 8.6B parameters. Further analysis reveals a structure-reasoning disconnection phenomenon in existing methods: external symbolic constraints and chain-of-thought (CoT) reasoning can improve reasoning steps, yet fail to enhance deep causal deductive capability. By internalizing temporal causal reasoning capabilities into the model, TCR-Former effectively bridges this gap, providing an efficient and reliable end-to-end foundation for complex reasoning scenarios.

Humans have a remarkable ability to judge causal relationships from a limited number of unreliable observations. Past work on causal cognition has largely focused on normative accounts of human behavior, leaving unknown how biologically plausible neural systems could learn causal relationships from observations and update their representations of causal structure with additional evidence. Here, we leverage task-optimized recurrent neural networks to discover candidate implementation-level neural mechanisms of causal judgment. We propose a novel cognitive task in which a subject observes stochastic samples from an unknown causal structure (e.g. among variables A, B, and C with unknown causal relationships), and must judge whether a specific causal relationship is present given a query (e.g. "does A cause C?"). We found that, after training, recurrent neural networks perform the task with high accuracy, adopt strategies that incorporate the behavior of non-queried variables to form their judgments, and, despite being trained only on pairwise queries ("does A cause B?", "does C cause A?", etc), form implicit beliefs about the complete graphical structure underlying the observations. Lastly, we use dynamical systems analysis to identify a set of low-level neural mechanisms that implement causal judgment and representation of causal graphical structure. Together, these findings lay the groundwork for a "bottom-up" approach to causal cognition, providing a potential basis for subsequent experimental study in the brain.

Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy, 100% conflict detection recall, and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.

Accurately matching human intentions in 3D space is an important goal of artificial intelligence. Recently, 3D Intention Grounding (3D-IG) has emerged, aiming to localize target 3D objects given a natural-language intent. Unlike conventional visual grounding with descriptive referring expressions, 3D-IG intents are abstract and non-descriptive, making object localization substantially more challenging. This requires inferring latent functional requirements from non-descriptive intents and aligning them with object-level 3D representations. However, existing methods largely rely on implicit intent–object matching, leading to logical gaps and limited interpretability, robustness, and generalization. To address these challenges, we propose Chain-of-Causal Reasoning (CoCR), a causality-inspired functional dependency reasoning framework that explicitly bridges abstract intentions and candidate objects through intermediate functional requirements. Specifically, CoCR progressively decomposes complex intents into ordered functional requirements, thereby forming an explicit intent–function–object reasoning chain that links abstract intentions to object suitability. Building on this chain, we construct a causality-inspired functional dependency graph to model requirement--attribute relationships and introduce a causal-visual alignment module that aligns function-aware representations with the geometric-semantic features of 3D point clouds, enabling bidirectional verification between structured functional reasoning and visual evidence. Extensive experiments on 3D Intention Grounding and 3D Visual Grounding demonstrate that our method enhances intent-aware object localization.

Structural causal models (SCMs) provide a principled foundation for causal reasoning, and neural causal models (NCMs) extend this framework by parameterizing causal mechanisms with neural networks. The classical formulation of NCMs assumes a specific architectural choice: a single function approximator per structural equation. In this work, we revisit this assumption and show that it represents only one point in a broader family of valid neural causal model parameterizations. Using interventions as an analytical lens and graph-based neural architectures as a concrete construction, we characterize how different parameter-sharing schemes over sub-mechanisms induce distinct members of the NCM family while preserving the same causal graph. This perspective yields two additional regimes beyond the classical formulation, which differ in how causal mechanisms are decomposed and parameterized within a unified structural framework. Together, these regimes demonstrate that architectural choices shape the causal semantics and expressivity of neural causal models, even when graphical structure is fixed. Relaxing structural assumptions further, we identify a class of partially causal models (PCMs) that support limited interventional or counterfactual queries without satisfying full SCM requirements, offering alternative trade-offs between causal fidelity and tractability. We place NCMs and PCMs within a unified expressivity spectrum between purely associational models and fully causal generative models, and illustrate these distinctions empirically using a graph-based variational autoencoder with interventional structure.

Investigating the causal structures of transformers often entails a trade-off between the granularity of the explanation and computational overhead. Existing approaches either bypass the full internal chain, potentially missing critical decision-relevant links, or struggle with the combinatorial explosion of path enumeration. To address these challenges, we propose an efficient automated path-tracing method that ensures both causal reliability and algorithmic feasibility. Through extensive verification, we find that (i) the identified paths are the primary sources for decision signals, as evidenced by a marked decrease in self-repair compared to non-path components; and (ii) transformers utilize modular, class-shared path mechanisms to process categorical information. Beyond these findings, we show that the potential implications of our method can stem from sparse manipulation, highlighting efficient applicability to downstream tasks such as model debugging and pruning.

Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and feedback loops are present. Existing approaches typically assume acyclicity or rely on global causal structure learning, limiting applicability and computational efficiency. In this work, we study a local, data-driven method for covariate selection based on conditional independence information. While this method is known to be sound and complete in acyclic causal models, its validity in the presence of cycles has remained unclear. Our main contribution is to show that these guarantees extend to cyclic causal models. In particular, our result relies on the invariance of conditional independence assertions under $\sigma$-acyclification. These findings establish a unified, cycle-agnostic perspective on covariate selection and causal effect estimation, showing that the method applies across cyclic and acyclic settings without modification. Empirically, we validate this on extensive synthetic data, showing reliable performance in cyclic causal models.

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models with covariate effects. We propose a novel **Co**variate-Adjusted **DE**ep C**A**usal **L**earning (**CoDEAL**) framework, that cohesively deal with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-output autoencoder) to form a heterogeneous causal panel model. The nonlinear covariate component flexibly captures complex covariate influences on outcomes, and the nonlinear factor decomposition enables CoDEAL to effectively capture both cross-sectional and temporal dependencies inherent in the data panel. This latent structural information is subsequently integrated into a customized matrix completion algorithm, thereby facilitating more accurate counterfactual imputation. Moreover, the use of a multi-output autoencoder explicitly accounts for heterogeneity across units and enhances the model interpretability of the latent factors. We establish theoretical guarantees on the convergence of the estimated counterfactuals and demonstrate the compelling performance of the proposed method using extensive simulation studies and real-world data applications.

Network-level traffic signal control (TSC) involves coordination under partial observability and strict real-time constraints. In real-world urban networks with dozens or hundreds of intersections, direct coordination incurs an intractably large joint action space, making centralized cooperation difficult to deploy in practice. We propose CorridorLight, which tackles this challenge by casting cooperation as task negotiation. A high-level Corridor Agent represents the network as a direction-level graph and periodically generates a small set of junction-disjoint corridor tasks via a seed-and-grow procedure. These tasks provide lane-level preference guidance, while Intersection Agents maintain fast decentralized control and selectively support the active corridor task. By learning when to cooperate versus act selfishly, CorridorLight enables an adaptive trade-off between local delay minimization and corridor-level relief, leading to improved global performance. To this end, we derive a closed-form, Lipschitz-continuous gating function by maximizing a counterfactual net gain between cooperative and selfish behavior, yielding smooth, interpretable cooperation without brittle heuristics. Experiments on synthetic grids and real-world networks show consistent system-level improvements and outperform cooperative TSC baselines across diverse topologies and demand patterns.

Federated Learning (FL) enables collaborative model training across clients while preserving data privacy. However, FL typically relies on voluntary client participation with uniform rewards, which can lead to high-quality clients dropping out due to inadequate incentives and low-quality clients taking a free-ride, both of which degrade overall model performance. However, existing incentive mechanisms fail to capture the true contribution value of a client’s data to the global model, and often skew client selection toward cost compression rather than quality enhancement, thereby exacerbating confounding bias. To address this, we propose CausalAFL, a causal-based framework that reduces information asymmetry and corrects for confounding factors. Specifically, CausalAFL builds a complete causal chain connecting client information, bid, and marginal contribution. By jointly optimizing an inference network and a generative network within a variational inference framework, it precisely adjusts for causal bias. Maximizing the evidence lower bound objective allows CausalAFL to separate true contribution from noise and highlight the value of high-quality clients. A social welfare objective that quantifies both natural direct and indirect effects guides the server to prioritize these clients, resulting in improved global model accuracy and social welfare. Experiments on multiple datasets demonstrate that CausalAFL can guide clients to adjust their bids, leading to winners with higher contributions, confirming its effectiveness.

Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal structure in terms of a low-dimensional one. Existing methods for learning such summaries from data assume that both the high- and low-dimensional structures are acyclic, which is helpful for causal effect identification and reasoning but excludes many high-dimensional models and thus limits applicability. We show that in the linear non-Gaussian (LiNG) setting, the high-dimensional acyclicity assumption can be relaxed while still allowing recovery of a low-dimensional causal directed acyclic graph (DAG). We further connect identifiability of this low-dimensional DAG to existing results: LiNG models with cycles are observationally identifiable only up to an equivalence class whose members differ by reversals of directed cycles; our low-dimensional DAG, which is invariant across all members of a given equivalence class, thus forms a natural representative of the class. While existing approaches for learning this observational equivalence class over high-dimensional variables have exponential time complexity, our low-dimensional summary is learned in worst-case cubic time and comes with explicit bounds on the sample complexity. We provide open source code and experiments on synthetic data to corroborate our theoretical results.

Randomized trial findings are routinely generalized to broader target populations to inform decisions in medicine and public policy. When the trial sample is misaligned with the target population, generalized effect estimates can yield suboptimal decisions. We identify and characterize the target subgroups that are underrepresented in the trial cohort in time-to-event settings. Here, underrepresentation arises in two distinct ways: baseline underrepresentation, when a subgroup's covariate profile is rare in the trial relative to the target, and time-varying underrepresentation, when differential censoring erodes a subgroup over follow-up. Existing diagnostics either conflate these mechanisms or address only the baseline case. We show that the asymptotic variance of the efficient generalizability estimator admits a decomposition in which only two terms can diverge as representation weakens: a sampling-weight term capturing baseline misalignment and a censoring-weight term capturing differential loss to follow-up. Underrepresented subgroups are therefore precisely those the trial estimates with poor precision, and the decomposition reveals which channel is responsible.We exploit this operationally in $\textbf{TRACE}$ ($\textbf{T}$emporal $\textbf{R}$ashomon $\textbf{A}$nalysis of $\textbf{CE}$nsored data), which combines a self-normalized variance objective with a tree-based search to localize underrepresented subgroups, attribute each to baseline mismatch or censoring, and pinpoint the follow-up intervals where censoring drives the variance inflation; the joint problem is solved by a parametric dynamic program nested inside a Rashomon ensemble of near-optimal trees. On synthetic benchmarks and a case study generalizing the National Job Training Partnership Act (JTPA) Study to U.S. JTPA-eligible adults, the framework recovers underrepresented regions, correctly attributes each mechanism, and identifies the follow-up windows driving temporal underrepresentation; refining the trial and target samples to reliably generalizable subgroups substantially reduces generalized effect-estimate variance.

Dense video captioning describes multiple events in long videos. However, each event is predicted independently, ignoring the causal relationships with the other events in the video. These causal relationships are important for human-like descriptions of long videos. Our experiments show that exisiting vision-language models fail at predicting causal relationships in long-form videos. No prior work jointly generates captions and event timestamps and predicts causal relationships over multi-event sequences in long-form videos. We address this through two contributions. First, we create a benchmark dataset with 21.6K videos and 85K events by using a large language model to generate annotations from dense captions in YouCook2 and ActivityNet. We validate this benchmark dataset through multimodal alignment, vision-language models, and human judgment. Second, we propose a novel architecture for dense causal captioning that not only generates captions but also learns causal relationships. This novel architecture not only predicts causality effectively (0.73 F1 ActivityNet, 0.64 F1 YouCook2) but also improves the vision-language model's core capabilities: caption quality increases by +6.3 CIDEr on ActivityNet and +7.4 on YouCook2, while temporal grounding becomes accurate as causal predictions force precise event boundary localization.

We introduce *causal pieces*, a novel concept for analysing spiking neural networks (SNNs), inspired by *linear pieces* used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike coding into distinct regions where the same subnetwork causes the output spikes. For networks of current-based leaky integrate-and-fire (LIF) neurons with large membrane time constants, we show that within each causal piece, output spike times are locally Lipschitz continuous with respect to inputs and network parameters. We further prove a lower bound on the approximation error that depends on the number of causal pieces. Thus, the number of causal pieces is a measure of the approximation capabilities of SNNs, which is valid despite spike-time discontinuities and applies to networks with both excitatory and inhibitory synapses. Empirically, we find that parameter initialisations yielding more causal pieces on the training set strongly correlate with SNN training success across multiple benchmarks, including Yin-Yang, Fashion-MNIST, and EuroSAT. Moreover, simulations with standard single-spike LIF neurons indicate that our findings extend beyond the theoretically analysed regime. These results establish causal pieces as a powerful and principled tool for analysing and improving the computational capabilities of SNNs.

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.

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.