Can AI Write Your Code?

2026-05-25 · Source: Towards Data Science · Field: Science & Research — Mathematics & Computational Sciences, Social Sciences & Behavioral Studies, Health & Medical Research · Depth: Expert, long

Summary

A study by Winberg et al., published on January 22, 2026, in Health Economics Review, evaluates ChatGPT-4.0 Pro's capability to generate code for complex causal inference methods in Python, R, and Stata. Focusing on Difference-in-Differences, Inverse Probability Treatment Weighting, and Regression Discontinuity, the research compares AI-generated code against benchmark solutions from Causal Inference: The Mixtape. Unlike prior subjective assessments, this study employs a structured methodology, using expert-crafted prompts for full coding workflows, including data management and figure generation. Performance was assessed across five indicators: accuracy, efficiency, error output, editing, and consistency. The findings suggest ChatGPT-4.0 Pro is more reliable for these tasks in Python and R compared to Stata, a result attributed to the greater abundance of public code examples for the former languages. The article also notes a personal shift in professional workflows towards Python and VS Code, influenced by LLM performance.

Key takeaway

For quantitative researchers integrating AI coding assistants, prioritize rigorous validation of AI-generated code, especially for complex econometric methods. While tools like ChatGPT-4.0 Pro can accelerate tasks in Python and R, human supervision remains critical to prevent errors and ensure accuracy. Consider shifting your workflow to environments like VS Code and languages like Python where LLMs demonstrate higher reliability, but always cross-reference outputs with established benchmarks. Your expertise in validating assumptions and results is more important than ever.

Key insights

Trusting AI-generated code for complex quantitative research requires rigorous validation against benchmarks and expert human supervision.

Principles

• AI coding reliability varies by language and task complexity.
• Benchmark-based evaluation is crucial for AI code quality.
• Human validation is essential for AI-generated code.

Method

Winberg et al. prompted ChatGPT-4.0 Pro with causal inference problem sets in Python, R, and Stata, then executed the generated code and compared outputs against "Causal Inference: The Mixtape" benchmarks.

In practice

• Shift to Python/VS Code for better LLM integration.
• Accelerate literature review and data collection with LLMs.
• Use LLMs for data processing, modeling, and reporting drafts.

Topics

• AI Code Generation
• Causal Inference
• ChatGPT-4.0 Pro
• Econometrics
• LLM Evaluation
• Python/R Programming

Best for: AI Scientist, Data Scientist, Research Scientist, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.