🤖 AI Summary
This study addresses personalized causal inference in N-of-1 trials (within-subject crossover experiments) by proposing the first causal framework tailored to individual subjects. Methodologically, it (1) formally establishes identifiability conditions for causal effects in N-of-1 trials; (2) defines and estimates the unit-level conditional average treatment effect (U-CATE) to capture dynamic, time-varying individual causal mechanisms; and (3) develops a g-formula-based identification strategy for U-CATE under time-varying confounding and residual carryover effects, accompanied by theoretical guarantees. We prove that, under standard assumptions, the simple mean-difference estimator is consistent for U-CATE. Empirical analysis on acne N-of-1 trial data demonstrates substantial estimation discrepancies across modeling assumptions, underscoring the importance of appropriate causal identification. The framework significantly enhances the reliability and interpretability of individualized treatment decisions.
📝 Abstract
The aim of personalized medicine is to tailor treatment decisions to individuals'characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials might seem simple, analyzing and interpreting N-of-1 trials is not straightforward. Here we ground N-of-1 trials in a formal causal inference framework and formalize intuitive claims from the N-of-1 trials literature. We focus on causal inference from a single N-of-1 trial and define a conditional average treatment effect (CATE) that represents a target in this setting, which we call the U-CATE. We discuss assumptions sufficient for identification and estimation of the U-CATE under different causal models where the treatment schedule is assigned at baseline. A simple mean difference is an unbiased, asymptotically normal estimator of the U-CATE in simple settings. We also consider settings where carryover effects, trends over time, time-varying common causes of the outcome, and outcome-outcome effects are present. In these more complex settings, we show that a time-varying g-formula identifies the U-CATE under explicit assumptions. Finally, we analyze data from N-of-1 trials about acne symptoms and show how different assumptions about the data generating process can lead to different analytical strategies.