ICLR 2026 Papers

Learning the dynamic causal structure is a difficult challenge in discovering causality from time series. Most existing studies rely on distributional or structural invariance to uncover the underlying causal dynamics, assuming stationary or partially stationary causality, which frequently conflicts with complex causal relationships in the real world. This boosts temporal causal discovery to encompass fully dynamic causality, where both instantaneous and lagged causal dependencies may change over time, bringing significant challenges to the efficiency and stability of causal discovery. To tackle these challenges, we introduce DyCausal, a dynamic causal structure learning framework that leverages convolutional networks to effectively model causal structures within coarse-grained time windows, and introduces linear interpolation to refine causal structures to each time step and recover time-varying causal graphs. In addition, we propose an acyclic constraint based on matrix norm scaling. It is more stable both theoretically and empirically, and constrains loops in dynamic causal structures with improved efficiency. Evaluations on both synthetic and real-world datasets prove that DyCausal significantly outperforms existing methods and identifies fully dynamic causal structures from coarse to fine.

Causal reasoning lies at the heart of robust and generalizable decision-making, and the *Pearl Causal Hierarchy* provides a formal language for distinguishing between observational ($\mathcal{L}_1$), interventional ($\mathcal{L}_2$), and counterfactual ($\mathcal{L}_3$) levels of reasoning. Existing bandit algorithms that leverage causal knowledge have primarily operated within the $\mathcal{L}_1$ and $\mathcal{L}_2$ regimes, treating each realizable and physical intervention as a distinct arm. That is, they have largely excluded counterfactual quantities due to their perceived inaccessibility. In this paper, we introduce a *counterfactual structural causal bandit* (ctf-SCB) framework which expands the agent's feasible action space beyond conventional observational and interventional arms to include a class of realizable counterfactual actions. Our framework offers a principled extension of structural causal bandits and paves the way for integrating counterfactual reasoning into sequential decision-making.

Estimating causal effects becomes particularly challenging when outcomes possess complex, non-Euclidean structures, where conventional approaches often fail to capture meaningful structural variation. We introduce a novel framework for topological causal inference, defining treatment effects through changes in the underlying topological structure of outcomes. In our framework, intervention-driven topological shifts across homology are summarized via power-weighted silhouettes. We propose a doubly robust estimator, derive its asymptotic properties, and develop a formal test for the null hypothesis of no topological effect. Empirical studies demonstrate that our approach reliably quantifies treatment effects and remains robust across diverse, complex outcome spaces.

A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encode. Here we ask whether such interventions create out-of-distribution (divergent) representations, and whether this raises concerns about how faithful their resulting explanations are to the target model in its natural state. First, we demonstrate theoretically and empirically that common causal intervention techniques often do shift internal representations away from the natural distribution of the target model. Then, we provide a theoretical analysis of two cases of such divergences: "harmless" divergences that occur in the behavioral null-space of the layer(s) of interest, and "pernicious" divergences that activate hidden network pathways and cause dormant behavioral changes. Finally, in an effort to mitigate the pernicious cases, we apply and modify the Counterfactual Latent (CL) loss from Grant (2025) allowing representations from causal interventions to remain closer to the natural distribution, reducing the likelihood of harmful divergences while preserving the interpretive power of the interventions. Together, these results highlight a path towards more reliable interpretability methods.

When a predictive model anticipates an undesired future event, a question arises: what can we do to avoid it? Resolving this forward-looking challenge requires determining the variables that positively influence the future, moving beyond statistical correlations typically exploited for prediction. In this paper, we introduce a novel framework for evaluating the influence of actionable variables in successfully avoiding the undesired future. We quantify influence as the degree to which the probability of success can be increased by altering variables based on the principle of maximum expected utility. While closely related to causal effects, our analysis reveals a counterintuitive insight: influential variables may not necessarily be those with intrinsically strong causal effects on the target. In fact, due to the dynamics of the decision process, it can be highly beneficial to alter a weak causal factor, or even a variable that is not an intrinsic factor at all. We provide a practical implementation for computing the proposed quantity using observational data and demonstrate its utility through empirical studies on synthetic and real-world applications.

We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our approach leverages both observed contextual information and known causal graph structures to guide the search. Within this framework, we propose a novel algorithm that jointly optimises over policies and the sets of variables on which these policies are defined. This thereby extends and unifies two previously distinct approaches: Causal Bayesian Optimisation and Contextual Bayesian Optimisation, while also addressing their limitations in scenarios that yield suboptimal results. We derive worst-case and instance-dependent high-probability regret bounds for our algorithm. We report experimental results across diverse environments, corroborating that our approach achieves sublinear regret and reduces sample complexity in high-dimensional settings.

Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring on popular yet incomplete hypotheses. We propose to discover the unknown effects of a treatment directly from data. For this, we turn unstructured data from a trial into meaningful representations via pretrained foundation models and interpret them via a Sparse Auto Encoder. However, discovering significant causal effects at the neural level is not trivial due to multiple-testing issues and effects entanglement. To address these challenges, we introduce _Neural Effect Search_, a novel recursive procedure solving both issues by progressive stratification. After assessing the robustness of our algorithm on semi-synthetic experiments, we showcase, in the context of experimental ecology, the first successful unsupervised causal effect identification on a real-world scientific trial.