Generating inverse probability weights for marginal structural models with time-series cross-sectional panel data
Read OriginalThis article provides a detailed, code-heavy guide for implementing marginal structural models (MSMs) with inverse probability weights in R. It addresses causal inference challenges in time-series cross-sectional panel data, using examples like workday policies and national happiness, and covers DAGs, confounding, and statistical adjustments for time-varying treatments and outcomes.
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