Ideological Bias in LLMs' Economic Causal Reasoning
Donggyu Lee, Hyeok Yun, Jungwon Kim, Junsik Min, Sungwon Park, Sangyoon Park, Jihee Kim

TL;DR
This paper evaluates whether large language models exhibit ideological bias in economic causal reasoning, revealing systematic inaccuracies and directional skew favoring intervention-oriented perspectives.
Contribution
It extends the EconCausal benchmark to include ideology-contested cases and systematically assesses LLMs' ability to predict empirically supported causal directions.
Findings
Models perform worse on ideology-contested items.
Accuracy is higher when causal signs align with intervention-oriented expectations.
Incorrect predictions tend to lean intervention-oriented, unaffected by one-shot prompting.
Abstract
Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCausal benchmark with ideology-contested cases - instances where intervention-oriented (pro-government) and market-oriented (pro-market) perspectives predict divergent causal signs. From 10,490 causal triplets (treatment-outcome pairs with empirically verified effect directions) derived from top-tier economics and finance journals, we identify 1,056 ideology-contested instances and evaluate 20 state-of-the-art LLMs on their ability to predict empirically supported causal directions. We find that ideology-contested items are consistently harder…
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