Skill UI

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.

This guide details how to construct a sophisticated and rigorous robustness suite for top-tier academic econometrics journals (e.g., AEJ: Applied). It moves beyond simple regression tables, providing targeted checks against common methodological threats such as omitted variables, specification search, functional form choices, and incorrect statistical inference. The goal is to systematically demonstrate that the main point estimate is stable, reliable, and not an artifact of the research design.

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 authors targeting the American Economic Review (AER). This skill helps authors assess whether their economics manuscript meets AER's high bar for general interest, methodological rigor, and structural presentation. It covers journal positioning, identification strategy, required modern inference techniques, and how to re-frame niche field papers for a broad disciplinary audience.

This guide provides a comprehensive framework for conducting rigorous statistical analysis and ensuring methodological validity for empirical manuscripts. It covers establishing measurement validity (CFA, reliability), selecting appropriate estimators (SEM, HLM, IV), addressing biases (CMB, endogeneity), and robust reporting of complex effects (mediation, moderation) required for top-tier academic journals.

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

This guide assists researchers in strengthening the methodological rigor of academic work, particularly in political science. It provides structured methods for defending research design, covering rigorous causal identification for quantitative work (e.g., DiD, IV, RDD) and systematic case selection/process tracing for qualitative analysis. Use this when facing reviewer skepticism or preparing a pre-analysis plan to ensure the evidence credibly connects to the theoretical argument.

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.

This guide provides a rigorous framework for strengthening research design in complex social science manuscripts. It covers methodologies ranging from advanced quantitative causal inference (e.g., DID, IV, RDD) to systematic qualitative case selection and process tracing. It teaches how to defend assumptions, rule out rival explanations, and generalize findings beyond the specific case or setting.

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 detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.