The advent of widely available cell phone mobility data in the United States has rapidly expanded the study of everyday mobility patterns in social science research. A wide range of existing ...

Full Python implementation of Aymeric et al.'s (2025) methodology for investigating the causal impact of parental environment on student achievement. Implements OLS regression and IV-2SLS instrumental ...

If you’re new to Python, one of the first things you’ll encounter is variables and data types. Understanding how Python handles data is essential for writing clean, efficient, and bug-free programs.

Latent variable modeling comprises a suite of methodologies that infer unobserved constructs from observable indicators, thereby enabling researchers to quantify abstract phenomena across diverse ...

ABSTRACT: The promotion of sustainable agricultural practices is crucial for achieving environmental sustainability. Moreover, there is limited documentation on how green agriculture moderates the ...

We publish deeply researched (and often vastly underread) academic papers about our collective omnipresent media bias. byTech Media Bias [Research Publication]@mediabias byTech Media Bias [Research ...

Abstract: Treatment effect estimation from observational data is a fundamental problem in causal inference, and its critical challenge is to address the confounding bias arising from the confounders.

The ISCHEMIA Trial randomly assigned patients with ischemic heart disease to an invasive treatment strategy centered on revascularization with a control group assigned non-invasive medical therapy. As ...