“Causal inference aims not just to observe associations, but to untangle cause-and-effect by carefully adjusting for confounders, using tools like potential outcomes, directed graphical models, standardization, and propensity scores.” Fundamentals of Causal Inference: With R
A foundational insight on how robust causal analysis goes beyond mere correlation — it demands clear assumptions, rigorous statistical methods, and often simulation or real-data examples to identify true effects. Perfect for posts about data science, epidemiology, social research, or biostatistics. This enhanced description helps attract readers interested in causal modeling, counterfactual reasoning, confounding adjustment, and evidence-based research.
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