This workshop offers a hands-on introduction for social scientists seeking to apply causal inference methods to observational and secondary data. Following a recap of foundational concepts of causal ...
According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...
Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...
Abstract: Process execution time is a key performance indicator for evaluating bottlenecks in business processes. Cases and activities that exceed the specified time constraints can be seen as ...
What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...
Abstract: Data-driven soft sensor techniques are increasingly being applied in complex industrial environments, enabling the modeling of many previously intractable variables and playing a critical ...
ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...
In addition to providing a programmatic interface for popular causal inference methods, DoWhy is designed to highlight the critical but often neglected assumptions underlying causal inference analyses ...
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