metadata

license: gpl-3.0
language:
  - en
tags:
  - world-models
  - arc
  - abstraction-reasoning-corpus
  - reasoning
  - abstract-reasoning
  - logical-reasoning
  - causal-reasoning
  - counterfactual-reasoning
  - evaluation
  - benchmark
  - structural-causal-models
  - causal-discovery
  - test-time-training
  - few-shot-learning
  - in-context-learning
task_categories:
  - question-answering
pretty_name: CausalARC – Abstract Reasoning with Causal World Models

Note: This page is currently under construction. See our official project page here: https://jmaasch.github.io/carc/

Overview

Reasoning requires adaptation to novel problem settings under limited data and distribution shift. This work introduces CausalARC: an experimental testbed for AI reasoning in low-data and out-of-distribution regimes, modeled after the Abstraction and Reasoning Corpus (ARC). Each CausalARC reasoning task is sampled from a fully specified causal world model, formally expressed as a structural causal model (SCM). Principled data augmentations provide observational, interventional, and counterfactual feedback about the world model in the form of few-shot, in-context learning demonstrations. As a proof-of-concept, we illustrate the use of CausalARC for four language model evaluation settings: (1) abstract reasoning with test-time training, (2) counterfactual reasoning with in-context learning, (3) program synthesis, and (4) causal discovery with logical reasoning.

Pearl Causal Hierarchy: observing factual realities (L1), exerting actions to induce interventional realities (L2), and imagining alternate counterfactual realities (L3) [1]. Lower levels generally underdetermine higher levels.

This work extends and reconceptualizes the ARC setup to support causal reasoning evaluation under limited data and distribution shift. Given a fully specified SCM, all three levels of the Pearl Causal Hierarchy (PCH) are well-defined: any observational (L1), interventional (L2), or counterfactual (L3) query can be answered about the environment under study [2]. This formulation makes CausalARC an open-ended playground for testing reasoning hypotheses at all three levels of the PCH, with an emphasis on abstract, logical, and counterfactual reasoning.

The CausalARC testbed. (A) First, SCM M is manually transcribed in Python code. (B) Input-output pairs are randomly sampled, providing observational (L1) learning signals about the world model. (C) Sampling from interventional submodels M' of M yields interventional (L2) samples (x', y'). Given pair (x, y), performing multiple interventions while holding the exogenous context constant yields a set of counterfactual (L3) pairs. (D) Using L1 and L3 pairs as in-context demonstrations, we can automatically generate natural language prompts for diverse reasoning tasks.