Effects of Temporal Information Structure on Causal Explanations Generated by Large Language Models: Experimental Study Using Lung Cancer Incidence Data

BACKGROUND Large language models (LLMs) are increasingly applied to causal explanation in public health and epidemiology. However, recent studies suggest that LLMs exhibit instability in processing temporal information, which raises concerns about their suitability for generating coherent causal explanations. Despite this, little is known about how distinct components of temporal information influence LLM-generated causal explanations in epidemiological contexts. OBJECTIVE This study aimed to examine how event ordering and the validity and presence of explicit temporal information affect LLM-generated causal explanations of long-term lung cancer incidence trends. Specifically, we evaluated their effects on evidence grounding, causal alignment, and disease specificity. METHODS We used a 2×3 factorial design manipulating event ordering (chronological vs randomized) and explicit temporal information (accurate, inaccurate, or absent). Five LLMs (GPT-5, Gemini 3.5 Flash, Claude Opus 4.8, Qwen3-32B, and Llama 3.3 70B Instruct) generated 600 causal explanations of long-term lung cancer incidence trends using Surveillance, Epidemiology, and End Results (SEER) data and PubMed-derived event statements. The effects of ordering and information were assessed using regression and factorial ANOVA. RESULTS Randomized ordering showed higher evidence grounding than chronological ordering (adjusted odds ratio [aOR] 2.53, 95% CI 1.89-3.37; P<.001). Causal alignment was higher under chronological than randomized ordering across all 3 information conditions, with larger differences in the decreasing phase. Disease specificity was higher under randomized ordering only under accurate information. CONCLUSIONS More accurate or structured temporal information did not necessarily improve LLM-generated causal explanations. Randomized ordering was associated with greater evidence utilization, whereas chronological ordering supported causal alignment across all information conditions, particularly for decreasing trajectories. These findings suggest that temporal context should be structured not only to provide temporal information but also to support the intended characteristics of the resulting explanation.