Collaborative Indirect Treatment Comparisons with Multiple Distributed Single-arm Trials

Music City Center 

Unmeasured confounders can introduce significant bias in causal inference, leading to incorrect conclusions. To address this issue, we propose a novel calibration method leveraging multiple candidate negative control outcomes (NCOs), including potentially invalid ones. Unlike traditional proximal inference methods, our distributional NCO framework assumes that system bias arises from an underlying distribution, rather than treating it as a fixed parameter. We develop a semiparametric approach that effectively removes the influence of this bias distribution, enabling more robust statistical inference on causal effects. Our method provides a flexible way to correct for confounding while accommodating uncertainty in NCO validity. We evaluate its performance through simulations and a real-world application. The results highlight the effectiveness of our framework in improving causal effect estimation under unmeasured confounding. Our approach extends the applicability of negative control methods, offering a more generalizable solution for bias correction in observational studies.

Causal inference

Negative control outcomes

Bias correction 

Section on Nonparametric Statistics