@inproceedings{wang-etal-2026-illusion-evaluating,
title = "The Illusion of $\textit{What If}$: Evaluating the Breakdown of Counterfactual Reasoning in {LLM}s",
author = "Wang, Yucheng and
Du, Yuetian and
Liu, Zhengyi and
Zhang, Rongyu and
Zhao, Bing and
Yang, Boyu and
Kong, Ming and
Qu, Lin and
Wei, hu and
Liu, Jie and
Zhu, Qiang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.807/",
pages = "17106--17129",
ISBN = "979-8-89176-462-0",
abstract = "Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks largely target bounded settings with fixed variables or single gold outcomes, overlooking open-domain scenarios requiring causal-process evaluation. To this end, we present $\textbf{WhatIfBench}$, a diagnostic benchmark for open-domain, open-form, long-horizon counterfactual causal reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios. To evaluate free-form responses, we further propose $\textbf{PRISM}$, which first converts each natural-language explanation into a Response-Derived Semantic Causal Graph of events, states, and mechanisms. On top of this graph, PRISM then jointly applies a Process Metric assessing graph-level causal validity and a Rubric Metric assessing answer-level explanatory adequacy. Evaluating six frontier LLMs with this framework, we find that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62{\%} final score. Further analysis reveals persistent causal gaps, premise drift, and topology fragmentation, suggesting that fluent counterfactual narratives often mask fragile causal processes. The benchmark, code, and evaluation scripts are available at https://github.com/zju-gt/WhatIfBench."
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<abstract>Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks largely target bounded settings with fixed variables or single gold outcomes, overlooking open-domain scenarios requiring causal-process evaluation. To this end, we present WhatIfBench, a diagnostic benchmark for open-domain, open-form, long-horizon counterfactual causal reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios. To evaluate free-form responses, we further propose PRISM, which first converts each natural-language explanation into a Response-Derived Semantic Causal Graph of events, states, and mechanisms. On top of this graph, PRISM then jointly applies a Process Metric assessing graph-level causal validity and a Rubric Metric assessing answer-level explanatory adequacy. Evaluating six frontier LLMs with this framework, we find that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62% final score. Further analysis reveals persistent causal gaps, premise drift, and topology fragmentation, suggesting that fluent counterfactual narratives often mask fragile causal processes. The benchmark, code, and evaluation scripts are available at https://github.com/zju-gt/WhatIfBench.</abstract>
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%0 Conference Proceedings
%T The Illusion of What If: Evaluating the Breakdown of Counterfactual Reasoning in LLMs
%A Wang, Yucheng
%A Du, Yuetian
%A Liu, Zhengyi
%A Zhang, Rongyu
%A Zhao, Bing
%A Yang, Boyu
%A Kong, Ming
%A Qu, Lin
%A Wei, hu
%A Liu, Jie
%A Zhu, Qiang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-illusion-evaluating
%X Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks largely target bounded settings with fixed variables or single gold outcomes, overlooking open-domain scenarios requiring causal-process evaluation. To this end, we present WhatIfBench, a diagnostic benchmark for open-domain, open-form, long-horizon counterfactual causal reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios. To evaluate free-form responses, we further propose PRISM, which first converts each natural-language explanation into a Response-Derived Semantic Causal Graph of events, states, and mechanisms. On top of this graph, PRISM then jointly applies a Process Metric assessing graph-level causal validity and a Rubric Metric assessing answer-level explanatory adequacy. Evaluating six frontier LLMs with this framework, we find that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62% final score. Further analysis reveals persistent causal gaps, premise drift, and topology fragmentation, suggesting that fluent counterfactual narratives often mask fragile causal processes. The benchmark, code, and evaluation scripts are available at https://github.com/zju-gt/WhatIfBench.
%U https://aclanthology.org/2026.findings-emnlp.807/
%P 17106-17129
Markdown (Informal)
[The Illusion of What If: Evaluating the Breakdown of Counterfactual Reasoning in LLMs](https://aclanthology.org/2026.findings-emnlp.807/) (Wang et al., Findings 2026)
ACL
- Yucheng Wang, Yuetian Du, Zhengyi Liu, Rongyu Zhang, Bing Zhao, Boyu Yang, Ming Kong, Lin Qu, hu Wei, Jie Liu, and Qiang Zhu. 2026. The Illusion of What If: Evaluating the Breakdown of Counterfactual Reasoning in LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17106–17129, Budapest, Hungary. Association for Computational Linguistics.