Causal mediation analysis has gained increasing attention in recent years. This article guides empirical researchers through the concepts and challenges of causal mediation analysis. I first clarify the difference between traditional and causal mediation analysis and highlight the importance of adjusting for the treatment-by-mediator interaction and confounders of the treatment-mediator, treatment-outcome, and mediator-outcome relationships. I then introduce the definition of causal mediation effects under the potential outcomes framework and different methods for the identification and estimation of the effects. After that, I highlight the importance of conducting a sensitivity analysis to assess the sensitivity of analysis results to potential unmeasured confounding. I also list various statistical software that can conduct causal mediation analysis and sensitivity analysis and provide suggestions for writing a causal mediation analysis paper. Finally, I briefly introduce some extensions that I made with my colleagues, including power analysis, multisite causal mediation analysis, causal moderated mediation analysis, and relaxing the assumption of no post-treatment confounding.