ArticleHealth economics review2026
Can AI write your code? A case study of chatgpt's statistical coding capabilities for quantitative research.
Article in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent advancements in Artificial Intelligence (AI), particularly in large language models (LLMs) like OpenAI’s ChatGPT, have extended its applications well beyond simple dialogue generation. ChatGPT has shown potential in supporting data-driven decision-making. ChatGPT has gained traction in academia for its ability to generate code for data analysis, providing robust support for programming languages. This study aims to evaluate ChatGPT’s ability to generate code for causal inference and data analysis.
methodsThis study evaluates ChatGPT4.0 Pro’s performance in coding Difference-in-Differences (Diff-in-Diff ), Inverse Probability Treatment Weighting (IPTW), and Regression Discontinuity (RD) using problem sets and reference code from “Causal Inference: The Mixtape”. The evaluation was conducted in Python, Stata, and R. Researchers provided structured prompts and feedback, and a fourth researcher replicated all tasks to assess consistency. Primary outcomes included accuracy, efficiency, error output, editing needs, and inter-user consistency.
resultsChatGPT generated accurate code and results in R and Python for most tasks. However, it struggled with IPTW and performed less reliably in Stata. Errors were often related to data management or figure generation. Although ChatGPT could replicate correct results, the structure and syntax of its code varied across users and sessions.
conclusionsChatGPT shows strong potential as a supportive tool for econometric coding tasks in health economics, especially in Python and R. However, its output still requires human interpretation and validation. As generative AI continues to evolve, these tools hold promise for streamlining research tasks but remain supplementary to skilled human researchers in quantitative research.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.