# Introduction This code investigates the effectiveness of large language models (LLMs) in transforming causal domain knowledge into a representation that better aligns with recommendations from causal data science. ## method The approach consists of two main tasks. ### Experiment on Task~1: identifying if two entities represent different values of the same causal variable To generated data ```bash python src/CMR1/CMR1_data_generation.py ``` To sample the data ```bash python src/CMR1/CMR1_data_sampling.py ``` To run the experiment the data ```bash python src/CMR1/CMR1_Experiment.py ``` To get the cosine similarity ```bash python src/CMR1/get_cos_sim.py ``` ### Experiment on Task~2: identifying interaction entities which represent values of different causal variables simultaneously To generated data ```bash python src/CMR1/CMR1_data_generation.py ``` To sample the data ```bash python src/CMR2/CMR2_data_sampling.py ``` To run the experiment the data ```bash python src/CMR2/CMR2_Experiment.py ``` To get the cosine similarity ```bash python src/CMR2/get_cos_sim.py