Skill UI

A comprehensive guide for strengthening causal identification strategies (DID, IV, RDD, RCT) for high-stakes academic publications. It details modern econometric estimators, required diagnostic checks (pre-trends, density tests, weak-IV robustness), and the philosophical shift from technique to answering a big economic question, essential for submitting to top journals like the Quarterly Journal of Economics (QJE).

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

A comprehensive methodological guide for structuring the robustness checks and online appendix of high-level empirical economics manuscripts, specifically tailored to the standards of the Quarterly Journal of Economics (QJE). It provides a structured approach to defending the identification assumption, selection, measurement, and inference, ensuring the paper anticipates and addresses potential referee critiques.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

A comprehensive guide to the rigorous academic peer review process, exemplified by top-tier Recommender Systems conferences (RecSys). It details the double-blind review mechanics, key evaluation criteria (novelty, validity, reproducibility), the critical rebuttal phase, and the requirements for final publication in ACM Digital Library. Essential for researchers and students aiming to improve academic submission quality.

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

A comprehensive project management skill designed for academic submissions in Recommender Systems (RecSys). It guides users through the entire research lifecycle, from initial venue fit and data freezing to abstract submission, full paper writing, anonymous repository management, rebuttal drafting, and final camera-ready preparation. It provides backward-planning time offsets and risk identification.

This guide outlines the rigorous standards for quantitative analysis required in academic economic research, particularly dynamic modeling. It provides a framework covering systematic model calibration, moment estimation, numerical solution discipline, and establishing fully reproducible computation workflows, ensuring results are highly auditable and verifiable.

This guide establishes the rigorous policy for submitting computational and empirical papers to academic journals. It mandates that authors provide sufficient data, source code, and detailed documentation (including execution order, random seeds, and OS used) to ensure that research findings can be fully reproduced by others, adhering to global standards like Option C.

This strategy guides researchers in anticipating the specific, rigorous methodological objections raised by top-tier academic referees, especially in applied econometrics. It provides a systematic map connecting econometric designs (like DID, IV, RD) to common threats (e.g., measurement error, bias, specification issues). Users can pre-empt these criticisms by structuring their manuscript to include necessary defenses, robustness checks, and scope limitations before formal submission.

This guide outlines the comprehensive policy and architecture required for submitting a fully reproducible replication package to The Review of Economics and Statistics (REStat). It details how to structure the repository, including data, master scripts, and the README file, ensuring that every result can be traced and regenerated by other researchers.