The Battle For The Soul Of Causal Inference
Explore the decades-long intellectual rivalry shaping how we understand causality in data science. This analysis examines the fundamental tension between two giants of causal methodology: ...
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Association Does Not Imply Causation, Except When it Does – A Causal Inference Perspective
Delving into the critical distinction between correlation and causation, this article explores why establishing true causal relationships remains one of the most challenging aspects of ...
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Common DAG Structures–Confounding, Collider Bias, and Mediation
Unlock the power of Directed Acyclic Graphs (DAGs) in understanding complex causal relationships across research disciplines. This comprehensive introduction demonstrates how these visual tools revolutionize ...
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Causal Inference Guide: Books, Courses, and More
Causal inference is a critical framework used to understand cause-and-effect relationships between variables, going beyond simple correlations to determine if changes in one variable directly ...
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Selection Bias, A Causal Inference Perspective (With Downloadable Code Notebook)
Collider bias occurs when we condition on (or select based on) a variable that is influenced by both the exposure and outcome of interest. This ...
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The Influence of Confounding Variables in Observational Studies
Observational studies help identify associations when RCTs are impractical, but they are often challenged by confounding variables. A confounder is a factor linked to both ...
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Once Upon a Time Series
A Journey Through Causal Inference and Inspiration In 2015, on the brink of defending a dissertation in biostatistics, the author found new hope and direction. ...
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Crash course on confounding, bias, and deconfounding remedies using R
Confounding bias is one of the most ubiquitous challenges in estimating effects from observational (real-world data) studies. Confounding occurs when the relationship between a treatment ...
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