Causal inference for N-of-1 trials
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.