Research Methods & Causal Inference
This hub is for the part of analysis that sits before the model: research questions, identification, measurement, experimental design, preregistration, ethics and interpretation.
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- 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
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 observational machine learning.
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.
Uplift Modeling for Targeted Interventions
Uplift modeling estimates treatment effect heterogeneity so interventions can target the people, assets, or cases most likely to benefit.
Research 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, but in civilization itself.
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 global competitiveness.
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.
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 impact of mathematics on innovation and societal progress.
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. It emphasizes the importance of simplicity and clarity while cautioning against oversimplification that can lead to loss of essential detail ...
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 writing.
Ethics 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.
Why Data Ethics Matters in Machine Learning
Ethical considerations are critical when deploying machine learning systems that affect real people.
Navigating AI Fairness
Introduction
Ethical Considerations in AI-Powered Elderly Care
As AI revolutionizes elderly care, ethical concerns around privacy, autonomy, and consent come into focus. This article explores how to balance technological advancements with the dignity and personal preferences of elderly individuals.
The Fears Surrounding Artificial Intelligence
Delve into the fears and complexities of artificial intelligence and automation, addressing concerns like job displacement, data privacy, ethical decision-making, and the true capabilities and limitations of AI.
Ethics in Data Science
A deep dive into the ethical challenges of data science, covering privacy, bias, social impact, and the need for responsible AI decision-making.
The 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 ecosystem.