Causal Epidemiology and Advanced Analytics: Target Trial Emulation, G-Methods, and Reproducibility

This chapter introduces modern approaches for estimating causal effects from observational health data. The chapter distinguishes statistical association from causal inference and explains why regression coefficients should not automatically be interpreted as causal effects. Students learn to formulate explicit causal research questions by defining the target population, exposure or intervention, comparison group, follow-up period, and health outcome. The material introduces the counterfactual or potential outcomes framework, including the Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE), and Average Treatment Effect on the Treated (ATT). It also explains the fundamental assumptions required for causal identification, consistency, exchangeability, and positivity, and uses Directed Acyclic Graphs (DAGs) to represent causal relationships and identify potential confounders. These concepts establish the foundation for designing epidemiological analyses that address causal questions rather than merely describing associations.

The second major focus is target trial emulation and G-methods for controlling confounding, particularly when exposures and confounders change over time. Target trial emulation involves specifying the protocol of a hypothetical randomized controlled trial and then implementing its key components using observational data. The chapter covers eligibility criteria, intervention strategies, assignment procedures, time zero, outcome definitions, causal contrasts, and analysis plans, emphasizing the importance of aligning eligibility, exposure assignment, and follow-up start to prevent immortal time bias. Students then explore the G-formula, an extension of standardization that estimates population-level outcomes under hypothetical interventions. The material also explains Inverse Probability Weighting (IPW), which uses weights to create a pseudo-population with balanced covariate distributions, and Marginal Structural Models (MSMs), which estimate marginal causal effects after weighting. These methods are particularly important for time-varying confounding, where a variable affected by previous exposure also influences subsequent exposure and the outcome. Examples involving smoking and cardiovascular disease, as well as antiretroviral treatment and mortality among patients with HIV, illustrate how these methods can be implemented and interpreted using R.

The final component examines sensitivity analysis for unmeasured confounding and the reproducibility of epidemiological research. Students learn that even sophisticated causal methods cannot eliminate bias from confounders that were not measured or adequately controlled. The chapter introduces sensitivity analysis, bias parameters, bounding analysis, and the E-value to evaluate how strongly an unmeasured confounder would need to be associated with both exposure and outcome to explain an observed effect. For example, the document presents a hypothetical study with a Risk Ratio of 0.70 and an E-value of 2.21, illustrating how this measure helps assess the robustness of an apparent protective association. The chapter also emphasizes reproducible analytical workflows through preregistration, data dictionaries, documented data cleaning, version control, and sharing analytical code where appropriate. Reporting standards such as STROBE and GATHER are discussed alongside the importance of protecting patient privacy when sharing data. Overall, this chapter equips Statistics students with advanced skills for defining causal estimands, implementing target trial emulation and G-methods, evaluating unmeasured confounding, and producing transparent, reproducible analyses that support credible epidemiological inference and evidence-based public health decisions.