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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.
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 ...
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 ...
Reflection was essential to the advanced Java toolkit for years. Now it's being superseded by newer, safer options. Here's how to use MethodHandle and VarHandle to gain programmatic access to methods ...
ABSTRACT: This study assesses the effects of migrant remittances on inclusive growth in Africa. We applied the ordinary least squares method on a sample of 48 countries in Africa with daily data from ...
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