Causal inference is the process of determining whether and how one variable influences another, going beyond simple correlations and attempting to uncover cause-and-effect relationships. It plays a crucial role in fields like medicine, economics, and social sciences, where understanding the impact of interventions or policies is essential. Unlike traditional statistical analysis, causal inference requires careful consideration of study design, confounding factors, and the use of specialized methods such as randomized controlled trials, instrumental variables, and propensity score matching to draw valid conclusions about causality.