Skill UI

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

A comprehensive guide for structuring research manuscripts for top-tier academic journals (e.g., ASQ). It details the rigorous requirements for both qualitative (e.g., grounded theory, ethnography) and quantitative (e.g., causal inference, multilevel modeling) designs. Learn how to ensure theoretical fit, establish credibility, and design against alternative explanations to maximize the impact and rigor of your work.

A comprehensive guide for transforming complex regression results into self-contained, highly legible figures suitable for top-tier academic journals. It focuses on best practices for visualizing causal inference results (DID, RDD, IV), ensuring that the main findings can be understood by the reader simply by viewing the figure, independent of the surrounding text. Ideal for researchers in econometrics and social sciences.

A comprehensive guide detailing the best practices for drafting a rebuttal letter in response to single-blind peer reviews for applied econometrics journals. Covers critical areas like defending identification, improving inference, and ensuring robustness, while maintaining mandatory synchronization between the revised manuscript and the official data archive.

This skill provides comprehensive norms for executing and reporting robust, reproducible data analysis in comparative, cross-national, or formal-empirical studies. It guides authors on honestly reporting uncertainty, ensuring statistical robustness, performing rigorous cross-national inference, and integrating qualitative evidence through triangulation to meet the demanding standards of top academic journals.