This hub is for the part of analysis that sits before the model: research questions, identification, measurement, experimental design, preregistration, ethics and interpretation.
Start here
- Causal Feature Selection for Observational Machine Learning
- Counterfactual Evaluation of Decision Policies
- Preregistering Structural Equation Modeling
- Data Ethics in Machine Learning
Causal inference and study design
What a Before-and-After Testimonial Can Establish
A genuine improvement does not identify its cause. A worked probability model explains how selected starting measurements, natural variation, and selective r...
Read article
Berkson's Paradox: How Selecting the Cases Worth Looking At Invents Correlations
Among escalated tickets, severity and customer value are correlated at minus 0.55. Across all tickets they are independent. Nothing about the tickets changed...
Read article
Staggered Rollouts and Difference-in-Differences: When Two-Way Fixed Effects Get It Wrong
A feature rolls out to regions in three waves. The panel regression with region and month fixed effects says the effect is 0.6. The true average effect on th...
Read article
Propensity Scores: Matching, Weighting and the Estimator That Forgives One Mistake
Four estimators agree on the effect when both models are correct. Misspecify the outcome model and regression adjustment is off by 0.19; misspecify the treat...
Read article
Causal Feature Selection for Observational Machine Learning
Predictive feature selection is not enough when a model supports interventions. This article explains how causal thinking improves feature design in observat...
Read article
Counterfactual Evaluation for Decision Policies
Counterfactual evaluation helps teams estimate how a new decision policy might perform before deploying it to users, patients, customers, or operations.
Read article
Uplift Modeling for Targeted Interventions
Uplift modeling estimates treatment effect heterogeneity so interventions can target the people, assets, or cases most likely to benefit.
Read articleInterference in Experiments: When Treated Users Take What Control Users Would Have Bought
The ranking change raises purchase intent from 10 to 12 percent, and the A/B test reports a 20 percent lift in sales. Rolled out to everyone, it delivers 3 p...
Read articleResearch practice
The Intellectual Crisis: How Utilitarian Thinking is Destroying the Foundation of Human Progress
Short-term thinking and utilitarian pressures are undermining the very institutions that sustain human progress. This is a crisis not just in science policy,...
Read article
The Hidden Crisis: What Happens When We Stop Funding Fundamental Research
Fundamental research is often targeted for cuts due to its lack of immediate outcomes. But eliminating it risks innovation, education, crisis response, and g...
Read article
Preregistering Structural Equation Modeling (SEM) Studies: A Comprehensive Guide
Learn how to preregister your SEM study by systematically locking down modeling and analytic decisions to improve scientific transparency and reduce bias.
Read article
The Undervalued Power of Mathematics in Modern Society
Explore how mathematics shapes modern society across fields like technology, education, and problem-solving. This article delves into the often overlooked im...
Read article
Applying Einstein's Principle of Simplicity Across Disciplines
Albert Einstein's quote, "Everything should be made as simple as possible, but not simpler," encapsulates a fundamental principle in science and analytics. I...
Read article
Understanding the Use of Error Bars in Scientific Reporting
Introduction
Read article
Handling Missing Data in Clinical Research
Abstract
Read article
How to Write a Research Paper
Master the process of writing a research paper with tips on developing a thesis, structuring arguments, organizing literature reviews, and improving academic...
Read articleCritical Review of 'Bursting the (Filter) Bubble: Interactions of Members of Parliament on Twitter'
Introduction
Read articleData Communication: Preserve the Evidence
Good data communication preserves the structure of the evidence: the estimand, denominator, uncertainty, assumptions, and distinction between description, pr...
Read articleEthics and policy
Data Drift and Fairness: Monitoring Equity When Populations Change
A fair model at launch can become unfair in production when populations, behavior, policies, or measurement systems change.
Read articleWhy Data Ethics Matters in Machine Learning
Ethical considerations are critical when deploying machine learning systems that affect real people.
Read article
AI Fairness: Metrics, Trade-offs, Causal Assumptions, and Governance
Fairness in machine learning is often presented as though it were a technical property that can be measured once the model has been trained. That framing is ...
Read article
Ethics of AI and Sensing in Older-Adult Care
Ethical technology for older adults requires consent, privacy, proportional monitoring, accessibility, contestability, and attention to decision-making capac...
Read articleThe Risks and Limits of Artificial Intelligence
A sober analysis of AI risk requires separating present operational harms, labor-market effects, security risks, model limitations, environmental costs, and ...
Read articleEthics in Data Science
Ethics in data science is a question of governance, measurement, rights, incentives, and accountability, not a checklist added after a model is built.
Read articleThe Vulnerability of Large Language Models to the Closure of Open-Source Data Platforms
An in-depth exploration of how the closure of open-source data platforms threatens the growth of Large Language Models and the vital role humans play in this...
Read article