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Causal inference methods are gaining increasing prominence in pharmaceuticaldrug development in light of the recently published addendum on estimands andsensitivity analysis in clinical trials to the E9 guideline of theInternational Council for Harmonisation. The E9 addendum emphasises the need toaccount for post-randomization or `intercurrent' events that can potentiallyinfluence the interpretation of a treatment effect estimate at a trial'sconclusion. Instrumental Variables (IV) methods have been used extensively ineconomics, epidemiology and academic clinical studies for `causal inference',but less so in the pharmaceutical industry setting until now. In this tutorialpaper we review the basic tools for causal inference, including graphicaldiagrams and potential outcomes, as well as several conceptual frameworks thatan IV analysis can sit within. We discuss in detail how to map these approachesto the Treatment Policy, Principal Stratum and Hypothetical `estimandstrategies' introduced in the E9 addendum, and provide details of theirimplementation using standard regression models. Specific attention is given todiscussing the assumptions each estimation strategy relies on in order to beconsistent, the extent to which they can be empirically tested and sensitivityanalyses in which specific assumptions can be relaxed. We finish by applyingthe methods described to simulated data closely matching two recentpharmaceutical trials to further motivate and clarify the ideas

Connecting Instrumental Variable methods for causal inference to the Estimand Framework