Geometric Causal Models

2026-07-06 · Eli N. Weinstein, David M. Blei arxiv

Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric causal models (GCMs), a framework for causal inference from dependent data that exploits underlying symmetries of the data generating process. For example, in spatial data, we consider processes that are symmetric under translations, or in graph data, symmetric under permutations of the nodes. We show how symmetries, formalized with group theory, can enable causal identification and estimation. We deploy ergodic theory for amenable groups to establish identification, and combine geometric deep learning with scalable Bayesian inference for estimation. We recover i.i.d. causal models and do-calculus when the data is a sequence and the symmetry is permutation equivariance, and find novel types of causal models when we use alternate structures and symmetries. As an example, we construct a causal model that satisfies the symmetries of DNA. This GCM enables new estimators for the effects of genetic variation, combining deep functional genomics models to describe outcomes and DNA language models to describe propensities. We illustrate on semisynthetic data.

📄 PDF Abstract BibTeX arXiv:2607.05153

Code (0)

등록된 구현이 없습니다.

Tasks

Bayesian Inference Causal Inference

Causal Manifold Fairness: Enforcing Geometric Invariance in Representation Learning

2026-01-06 · Vidhi Rathore arxiv

Fairness in machine learning is increasingly critical, yet standard approaches often treat data as static points in a high-dimensional space, ignoring the underlying generative structure. We posit that sensitive attribut…

Representation Learning Causal Inference

Emergent Causal-Geometric Dynamics Across Depth in Large Language Models

2026-02-04 · Shahar Haim, Daniel C McNamee arxiv

Geometric analyses of large language model (LLM) representations reveal structured variation across depth but remain fundamentally correlational with respect to token prediction formation. Meanwhile, causal interventions…

Justifying Information-Geometric Causal Inference

2014-02-11 · Dominik Janzing, Bastian Steudel, Naji Shajarisales, Bernhard Schölkopf

Information Geometric Causal Inference (IGCI) is a new approach to distinguish between cause and effect for two variables. It is based on an independence assumption between input distribution and causal mechanism that ca…

Causal Inference

SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

2026-03-01 · Rong Fu, Chunlei Meng, Jinshuo Liu, Dianyu Zhao 외 arxiv

Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a unified framework combining hyperspherical representation learning with struct…

Representation Learning

Effective Connectivity-Based Neural Decoding: A Causal Interaction-Driven Approach

2016-07-24 · Saba Emrani, Hamid Krim

We propose a geometric model-free causality measurebased on multivariate delay embedding that can efficiently detect linear and nonlinear causal interactions between time series with no prior information. We then exploit…

Time Series Time Series Analysis