Introduction to Causal Inference and Causal Data Science

Day 3:
Target Trial Emulation

Bas B.L. Penning de Vries
Dept. Data Science & Biostatistics
Julius Center, UMC Utrecht

Test-your-knowledge quiz

Question 1

Which of the following options best describes the meaning of a potential outcome according to the counterfactual (or potential) outcomes framework?

  1. A possible value of the outcome variable
  2. The outcome of an individual that would be observed had treatment been set (by intervention) to a certain value
  3. The best outcome an individual can achieve
  4. A possible outcome of a study

Learning objectives

By the end of today, you’ll be able to

  • Describe what is meant by a target trial and target trial emulation
  • Identify key components of target trial emulation, including the determination of the start of follow-up (time zero)
  • Recognise a taxonomy of estimands relevant to target trial emulation and distinguish between common targets such as intention-to-treat and per-protocol effects (Friday!)
  • Describe the relevance of target trial emulation in causal inference from observational data
  • Recognise common deviations from a target trial in observational studies
  • Explain the basics of commonly used methods to address these deviations

“Causal inference from observational data can be viewed as
an attempt to emulate a hypothetical randomized trial”

Hernán and Robins, 2020, Causal Inference: What If

Why trials?

Causal inference is about speculating what would happen if …

A causal effect is a contrast between the answers to what-if questions.

Fundamental obstacle: impossible to observe the consequences of ≥ 2 mutually exclusive actions (interventions, treatments, etc.)