Time-varying confounding is a methodological challenge in causal inference, particularly in longitudinal studies with time-varying exposures. It refers to the phenomenon where factors influencing both subsequent exposure decisions and future outcomes change over time, and these factors are often themselves affected by prior exposures, creating a dynamic feedback loop that biases standard causal effect estimates. Addressing time-varying confounding is crucial for obtaining valid and unbiased estimates of causal effects from observational data, often requiring specialized statistical methods like G-computation or inverse probability weighting.