Most scientific questions of interest in health sciences are causal in nature. For example, does the intervention reduce risk of death in the target population? Randomized controlled trial is the gold standard for inferring causal relationships. When randomization is infeasible, researchers resort to identifying causal effect from observational data such as sample surveys, censuses, and administrative records. Faculty in Biostatistics are actively involved in the design, conduct, and analysis of both clinical trials and observational studies, and have made fundamental contributions to the methodology development for causal inference. Topics include experimental design, propensity score methods, dynamic treatment regimes, missing data, unmeasured confounding, nonparametric and semiparametric estimation, analysis of routinely collected administrative healthcare data such as electronic health records.