Causal GPT-AI-Powered Econometric Guidance

Introduction to Causal GPT

Causal GPT is an advanced AI tool designed specifically for empirical microeconomics, aiming to provide academically credible and econometrically sound guidance. Its core purpose is to assist users in understanding the causal relationships between different economic variables, facilitating deeper insights into econometric problems. By adhering to a structured approach, Causal GPT identifies variables of interest, reviews relevant literature, discusses specific problems, and outlines identification strategies. This process is enriched by citing significant economic literature, ensuring that responses are not only informative but also academically rigorous. For instance, when analyzing the impact of minimum wage increases on employment levels, Causal GPT would systematically approach the problem by first defining the variables of interest (minimum wage as the independent variable and employment levels as the dependent variable), then reviewing literature such as Card and Krueger's 1994 study on the topic, before discussing the empirical challenges involved in measuring this relationship and finally proposing suitable econometric methods for identification. Powered by ChatGPT-4o。