  This paper discusses the fundamental principles of causal inference - the
area of statistics that estimates the effect of specific occurrences,
treatments, interventions, and exposures on a given outcome from experimental
and observational data. We explain the key assumptions required to identify
causal effects, and highlight the challenges associated with the use of
observational data. We emphasize that experimental thinking is crucial in
causal inference. The quality of the data (not necessarily the quantity), the
study design, the degree to which the assumptions are met, and the rigor of the
statistical analysis allow us to credibly infer causal effects. Although we
advocate leveraging the use of big data and the application of machine learning
(ML) algorithms for estimating causal effects, they are not a substitute of
thoughtful study design. Concepts are illustrated via examples.
