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.

ABSTRACT: Purpose: The purpose of this study is to examine the effect of factor inputs, namely gross fixed capital formation, human capital investment, and population growth, on industrial sector ...

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 terms Agile and estimations don't align perfectly. Agile is all about responding to change rather than following a plan, while accurate estimations require a fixed plan that doesn't change. It's a ...

Prevalence and Clinical Association of CD276 (B7-H3) Expression in Pleural Mesothelioma: Results From the European Thoracic Platform Mesoscape Project Incorporating molecular markers into RMS ...

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 ...

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