Skill UI

This comprehensive suite provides systematic checks to ensure spatial estimates are robust against methodological choices. It addresses core econometric issues such as Modifiable Areal Unit Problem (MAUP), boundary arbitrariness, spatial sorting, and spillover effects. Use this guide when submitting research to high-impact journals (like JUE) to demonstrate the reliability and stability of spatial relationships.

A comprehensive guide on crafting convincing spatial exhibits for top-tier economics journals, especially those focusing on urban economics. This skill helps structure the necessary maps, regression tables, and event-study plots required to visually demonstrate the identifying variation (e.g., treatment boundaries, discontinuities). It focuses on best practices—such as reporting spatial standard errors and making maps do econometric work—rather than running the analysis itself.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.

A comprehensive pre-writing guide for researchers determining the optimal venue and track for a data science contribution. It outlines a two-stage decision process: first, assessing if the work aligns with the KDD focus on data regimes (scale, drift, heterogeneity); and second, selecting the correct track (Research, ADS, Datasets & Benchmarks, etc.) based on the strongest, most honest evidence available—especially emphasizing real-world deployment outcomes.

A comprehensive style guide for revising research papers to meet the specific, high standards of KDD conferences. It details the expected structure of the first page, emphasizing data regimes, named mechanisms, and quantifiable evidence (scale, metrics). It also provides rules for differentiating research vs. applied data science (ADS) voices and optimizing content for two-column, page-limited formats.

This skill helps authors assess the suitability of their quantitative marketing, econometrics, or industrial organization (IO) paper for the Marketing Science journal. It outlines the journal's high standards, focusing on rigorous formal contributions, credible identification, and appropriate structural framing. Use this tool for pre-submission self-checking, understanding the required methodology bar, and deciding on the best academic venue.

This guide details the rigorous review process for papers in Mathematical Finance, covering single-blind peer review standards, from editor screening to line-by-line proof verification by referees. Learn what reviewers prioritize—methodological novelty, proof correctness, and financial applicability—to avoid common submission pitfalls and improve your chances of acceptance.

This guide details best practices for generating self-contained, high-impact exhibits (tables and figures) for academic journals, particularly those with strict page limits like Management Science. It instructs users on structuring both theoretical proofs (analytical lane) and empirical results (e.g., regression coefficients, event-study plots) to ensure the findings are immediately readable and persuasive, even without reading the main text.

A comprehensive guide for structuring the theoretical core of academic papers in Management Science. It covers two main approaches: developing formal analytical models (assumptions, propositions, comparative statics) or generating robust empirical hypotheses based on explicit mechanisms. The goal is always to derive decision-relevant, cross-departmental insights.

This comprehensive guide assists authors in developing rigorous methodology sections for academic papers in Marketing Science. It covers advanced techniques such as structural econometrics, causal ML, dynamic games, and quasi-experiments. Key focus areas include ensuring model identifiability, specifying estimators (GMM, MLE), and rigorously defending exogeneity assumptions.

This guide serves as a comprehensive workflow router for authors preparing a Marketing Science manuscript. It dictates the sequential steps, from identifying a modeling-worthy topic and developing a structural or analytical model, through data analysis, contribution framing, and final submission. It ensures the paper maintains a focus on formal modeling and managerial counterfactuals, adhering to the journal's rigorous standards.