Randomization and Confounder Distribution
Randomization ensures that both measured and unmeasured confounders are evenly distributed across treatment groups in expectation, which is the fundamental principle that makes randomized controlled trials the gold standard for causal inference.
How Randomization Works
Randomization achieves balance of confounders through probability theory, not through measurement or adjustment:
Random allocation creates comparable groups by ensuring each participant has an equal probability of assignment to any treatment arm, which distributes all baseline characteristics—whether measured or not—evenly across groups in expectation 1
This balance occurs for unmeasured confounders precisely because randomization does not depend on knowing, measuring, or adjusting for specific variables 1
The process is probabilistic, meaning balance is achieved "on average" across hypothetical repetitions of the trial, though any single trial may have some imbalance by chance alone 1
Contrast with Observational Methods
The key distinction from non-randomized studies is critical to understand:
Propensity score analysis and regression modeling can only adjust for measured confounders, leaving unmeasured confounding as a persistent threat to validity 1
Statistical adjustment methods require knowing and measuring all confounders to achieve balance, which is impossible in practice since unmeasured confounders by definition cannot be included in any adjustment model 1
Observational studies must rely on sensitivity analyses to explore how unmeasured confounding might affect results, whereas randomized trials eliminate this concern through design 1
Important Caveats
Several practical considerations affect this theoretical advantage:
Subgroup analyses that exclude randomized participants (such as analyzing only those who reached embryo transfer rather than all randomized patients) destroy the balance created by randomization and reintroduce confounding 1
Small sample sizes may result in chance imbalances even with proper randomization, though these imbalances are not systematic and do not represent confounding bias 1
The balance applies to baseline confounders only—randomization does not protect against confounding by factors that occur after randomization, such as differential dropout or adherence 1