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.

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 ...

They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...

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.

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 ...

Nobel laureate Lars Peter Hansen, the David Rockefeller Distinguished Service Professor in Economics and Statistics at the University of Chicago, shared the Sveriges Riksbank Prize in Economic ...

Endogeneity presents a significant challenge in conducting causal inference in observational settings. Researchers in social sciences, statistics, and related fields have developed various ...

First Solar shares are up 45% year to date, roughly double the broad market's gain. We view the shares as overvalued, with our $190 fair value estimate trailing the PitchBook consensus average of $288 ...

This chapter synthesizes and critically reviews the modern instrumental variables (IV) literature that allows for unobserved heterogeneity in treatment effects (UHTE). We start by discussing why UHTE ...