Evidence-based medicine requires investigators to include the best available evidence into their decision-making process. The best evidence regarding the causal effect of an intervention or a treatment can be provided by properly conducted randomized clinical trials. Randomized trials, however, can be costly, infeasible, or unethical. In addition, results from trials may suffer from selection bias, because participants may not be representative of the real-world population. Data from, for example, electronic health records are more representative of real-world practice. However, despite their potential, they are observational, where confounding bias arises due to the presence of factors related to both the intervention and the outcome under study. In the last few decades, many techniques have been developed to estimate causal effects from observational real-world data. In this talk, I will briefly introduce techniques to identify causal effects from observational real-world data. I will then introduce statistical methods to estimate these causal effects, such as regression adjustment, inverse probability weighting, and doubly robust estimators. Finally, I will discuss practical considerations.