Although the ICH E9(R1) guideline introduces the estimand framework for clinical trials without explicitly using the term “causal,” it refers to treatment effects as how the outcome of treatment compares to what would have happened to the same subjects under an alternative treatment. This definition of estimands is aligned with the potential outcomes framework utilized in causal inference. Randomized controlled trials (RCTs) are the gold standard for estimating the average causal treatment effect in drug development. More broadly, causal inference plays a crucial role whenever care and assumptions are needed to account for confounding factors and selection biases, such as in observational studies or RCTs subject to intercurrent events. In this chapter, we will review causal inference and its assumptions, discuss the role it can have in drug development (in randomized trials and observational data analyses), and discuss the interplay between causal inference and the ICH E9(R1) guideline. In this context, we will stress the importance of well-defined interventions and estimands, proper trial designs, and transparent assumptions to ensure valid and reliable conclusions.