Building Object-based Causal Programs for Human-like Generalization

• URL: http://arxiv.org/abs/2111.12560v1
• Date: Sat, 20 Nov 2021 15:21:17 GMT
• Title: Building Object-based Causal Programs for Human-like Generalization
• Abstract summary: We present a novel task that measures how people generalize objects' causal powers. We propose a computational modeling framework that can synthesize human-like generalization patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may navigate the compositional space of possible causal functions and categories efficiently. Our modeling framework combines a causal function generator that makes use of agent and recipient objects' features and relations, and a Bayesian non-parametric inference process to govern the degree of similarity-based generalization. Our model has a natural "resource-rational" variant that outperforms a naive Bayesian account in describing participants, in particular reproducing a generalization-order effect and causal asymmetry observed in our behavioral experiments. We argue that this modeling framework provides a computationally plausible mechanism for real world causal generalization.

Related papers

• Relational Structural Causal Models [52.818153111470394]
  We develop relational structural causal models, extending structural causal models to settings where objects and their relations vary.<n>We propose relational neural causal models, a provably correct approach that outperforms non-relational baselines on simulated traffic scenes.
  arXiv  Detail & Related papers  (2026-06-12T18:54:06Z)
• Toward a Theory of Generalizability in LLM Mechanistic Interpretability Research [3.1304584228602685]
  I propose five axes of correspondence along which mechanistic claims might generalize.<n>I analyze "1-back attention heads" across pretraining in random seeds of the Pythia models.
  arXiv  Detail & Related papers  (2025-09-26T18:38:16Z)
• On the Identifiability of Causal Abstractions [15.785002371773139]
  Causal representation learning enhances machine learning models' robustness and generalizability. We focus on a family of CRL methods that uses contrastive data pairs in the observable space to identify the latent causal model. We introduce a theoretical framework that calculates the degree to which we can identify a causal model, given a set of possible interventions.
  arXiv  Detail & Related papers  (2025-03-13T19:34:05Z)
• Counterfactual Generation from Language Models [64.55296662926919]
  We show that counterfactual reasoning is conceptually distinct from interventions. We propose a framework for generating true string counterfactuals. Our experiments demonstrate that the approach produces meaningful counterfactuals.
  arXiv  Detail & Related papers  (2024-11-11T17:57:30Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• CIParsing: Unifying Causality Properties into Multiple Human Parsing [82.32620538918812]
  Existing methods of multiple human parsing (MHP) apply statistical models to acquire underlying associations between images and labeled body parts. We present a causality inspired parsing paradigm termed CIParsing, which follows fundamental causal principles involving two causal properties for human parsing. The CIParsing is designed in a plug-and-play fashion and can be integrated into any existing MHP models.
  arXiv  Detail & Related papers  (2023-08-23T15:56:26Z)
• Hierarchical Neural Simulation-Based Inference Over Event Ensembles [0.4515457784397788]
  We introduce approaches for dataset-wide probabilistic inference in cases where the likelihood is intractable. We construct neural estimators for the likelihood(-ratio) or posterior and show that explicitly accounting for the model's hierarchical structure can lead to significantly tighter parameter constraints.
  arXiv  Detail & Related papers  (2023-06-21T21:50:42Z)
• Causal Discovery in Linear Structural Causal Models with Deterministic Relations [27.06618125828978]
  We focus on the task of causal discovery form observational data. We derive a set of necessary and sufficient conditions for unique identifiability of the causal structure.
  arXiv  Detail & Related papers  (2021-10-30T21:32:42Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• A Self-Supervised Framework for Function Learning and Extrapolation [1.9374999427973014]
  We present a framework for how a learner may acquire representations that support generalization. We show the resulting representations outperform those from other models for unsupervised time series learning.
  arXiv  Detail & Related papers  (2021-06-14T12:41:03Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.