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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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The Hidden Variable That Decides Whether a Food Label Works: Your Working Memory
Published:
A trial found that whether a detailed or simple calorie label works better depends on something no nutrition label has ever measured: how much you can hold in your head in the moment.
A Food Label That Warned Us in 2012 Stopped Warning Us by 2018 — Here’s How We Know
Published:
You can’t run a controlled trial on a label the whole country has already seen for years. Here’s how six years of real shopping data revealed a label quietly losing its effect.
Moms pursuing a PhD are alone in the crowd?
Published:
This is my second year as a PhD student and Mum. In this year I have achieved the goal I had hoped for: writing my first research paper. But is it the real achievement I wanted? Definitely not!
FOOD LABELLING: DO WE INTERPRET NUTRITION LABELS CORRECTLY?
Published:
The blog explains that food labels are a ubiquitous feature of contemporary food retailing and, as such, are commonplace for most people. However, some of us deliberately avoid food labels, while others have difficulty understanding their content or encounter barriers to using them effectively.
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statistical_analysis
Causal Inference in Python
Python provides an array of tools for sophisticated causal analysis, often utilized alongside machine learning paradigms.
Causal Inference in R
Causal inference can be seen as a subfield of statistical analysis. It is used in various fields such as econometrics, epidemiology, educational sciences, etc. With causal inference one addresses questions about effects of a treatment, intervention, or policy on some target over a given sample or population. Under certain identifiability and model assumptions, causal inference can be carried out by fitting simple regression models or combining several regression models in a specific way as will be sketched out later. For observational data, additional untestable assumptions have to be made to (non-parametrically) identify causal effects.