Skill UI

This skill guides users through strengthening the causal identification strategy of empirical economic manuscripts (DiD, IV, RDD, Experiments). It helps audit research designs against general academic credibility standards, ensuring that assumptions, potential biases, and the link between data variation and causal objects are explicitly stated and robustly defended for diverse expert audiences.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

This guide outlines the rigorous quantitative standards required for publishing comparative and longitudinal studies in high-impact sociological journals. It covers essential techniques such as multilevel modeling, handling small macro-N inference, calculating marginal effects, and ensuring strict reproducibility. It teaches researchers how to design analyses that withstand expert double-blind review.

Provides rigorous guidelines for designing complex quantitative studies, including cross-national comparisons, longitudinal panels, and multilevel models. It emphasizes identifying mechanisms, assessing measurement equivalence, and defending causal claims against confounders in advanced sociological research.

This router guides researchers through the systematic process of preparing a manuscript for Experimental Economics (ExpEcon), the ESA's flagship journal. It identifies the correct specialized skills (e.g., theory modeling, robustness checks, referee strategy, rebuttal) needed at each stage, ensuring adherence to methodological rigor, including incentive compatibility and the no-deception norm. It is designed to help teams structure their research from initial design to final submission.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

Provides essential guidelines for designing rigorous, generalizable field experiments in crop research. Covers multi-environment trials (MET), proper randomization, blocking strategies, split-plot designs, and Genotype-by-Environment (G×E) analysis. Use this tool when planning or defending the scientific rigor of an agricultural manuscript.

This guide outlines advanced econometric strategies and best practices for establishing causal identification in corporate finance and asset pricing. It addresses common methodological pitfalls, such as endogeneity, spurious correlation, and weak designs, providing specific techniques (e.g., staggered DiD, robust IV inference) required to defend a research paper's claims to top-tier academic journals.

This guide is designed for authors targeting top-tier journals like Foundations of Computational Mathematics (FoCM). It outlines the journal's high academic standards, requiring deep mathematical rigor, established theorems, and proofs (e.g., convergence, complexity bounds) at the math-computation interface. It emphasizes theoretical foundations over empirical results, making it ideal for advanced research in numerical analysis, optimization theory, and computational geometry.

This guide provides methodological rigor for designing studies—including manipulative experiments, observational gradient analyses, and process modeling—in the context of global change biology. It helps researchers anticipate and address critical reviewer concerns regarding scale, replication, confounding factors, and the limits of causal inference, ensuring the design can support strong claims of driver-to-response relationships.

This skill provides a rigorous academic review framework for determining if a research paper significantly advances the frontiers of game theory and its applications. It assesses conceptual advances, mechanism design robustness, behavioral novelty, and cross-field reach, guiding authors on whether their work meets the high standards of top-tier academic journals like GEB. Ideal for assessing high-level theoretical or empirical contributions.