Correlation alone isn’t enough to make real-world decisions. In Part 2 of this series, we explore advanced causal inference concepts used in biostatistics, including propensity score methods, dynamic treatment regimes, missing data, unmeasured confounding, and nonparametric and semiparametric estimation. This episode explains how these tools help researchers strengthen causal interpretation, improve study validity, and support evidence-based conclusions in public health and applied research.

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