Are Large Language Models Capable of Causal Reasoning for Sensing Data Analysis?

The correlation analysis between socioeconomic factors and environmental impact is essential for policy making to ensure sustainability and economic development simultaneously. With the development of Internet of Things (IoT), citizen science IoT monitoring provides valuable environmental measurements, such as PM 2.5 for air quality monitoring. However, socioeconomic factors are usually interconnected and confound each other, making accurate correlation analysis challenging. To isolate this information on an individual socioeconomic factor, we need to mitigate the confounding effect (e.g., propensity score matching) of other factors on the environmental sensing data. Large language models (LLMs) have shown remarkable capabilities in data reasoning, making us wonder if they can conduct causal reasoning and answer questions like "What is the most important socioeconomic factor that impacts regional air quality?"

In this paper, we present a new evaluation framework named "Order-of-Thought" based on Bloom's Taxonomy pedagogical framework to quantify the LLMs' ability for causal reasoning. We apply this evaluation framework with both natural language-based and program-based prompting strategies. Our evaluation uncovers the exceptional potentials of LLMs in causal reasoning for sensing data analysis, offering valuable insights regarding their capabilities and limitations, and providing useful directions to further achieve a higher-order thought.