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.
Sequential causal effect estimation has recently attracted increasing attention from research and industry. While the existing models have achieved many successes, there are still many limitations.
\item How can we identify causal effects when we are in the presence of unobserved confounding? One popular way is to find and use \underline{\emph{\textbf{instrumental variables}}} ...
Visit Stata Example File and Python Example File. There is a step-by-step example. This project is licensed under the GNU Affero General Public License v3.0. See LICENSE for details. Note: Commercial ...
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 propose a new framework that addresses endogenous regressors using a novel conditional copula endogeneity model to capture the regressor-error dependence ...
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 ...
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 ...
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