ICML 2025

Compositional Causal Reasoning Evaluation in Language Models
Jacqueline R. M. A. Maasch 1 Alihan Hüyük 2 Xinnuo Xu 3 Aditya V. Nori 3 Javier Gonzalez 3
Abstract
Causal reasoning and compositional reasoning
are two core aspirations in AI. Measuring the ex-
tent of these behaviors requires principled eval-
uation methods. We explore a unified perspec-
tive that considers both behaviors simultaneously,
termed compositional causal reasoning (CCR):
the ability to infer how causal measures compose
and, equivalently, how causal quantities propagate
through graphs. We instantiate a framework for
the systematic evaluation of CCR for the average
treatment effect and the probability of necessity
and sufficiency. As proof of concept, we demon-
strate CCR evaluation for language models in the
Llama, Phi, and GPT families. On a math word
problem, our framework revealed a range of tax-
onomically distinct error patterns. CCR errors
increased with the complexity of causal paths for
all models except o1.
1. Introduction
Causal reasoning is a defining outcome of human evolution
(Goddu & Gopnik, 2024). Humans flexibly reason about
cause and effect in factual realities that can be observed and
intervened on, as well as imagined counterfactual worlds.
A causal lens enables humans and machines alike to learn
generalizable lessons about the mechanics of the universe
(Schölkopf et al., 2021). Thus, human-like AI might require
reasoning at all three levels of Pearl’s Causal Hierarchy: as-
sociational, interventional, and counterfactual (Pearl, 2000).
Human-like AI might also require compositional reasoning
(Lake et al., 2017): the capacity to recognize and synthe-
size novel combinations of previously observed concepts
(Xu et al., 2022). Compositionality is ubiquitous in the
physical world, symbolic systems, and human cognition
1Cornell
Tech
2Harvard
University
3Microsoft
Re-
search Cambridge.
Correspondence to:
Jacqueline Maasch
<maasch@cs.cornell.edu>. Work done while authors J. Maasch
an...