Adaptive Leveraged Causal Inference with an Application to PCR Testing

Oregon Convention Center 

Causal inference methods play a pivotal role in elucidating the effects of interventions and treatments in various domains including healthcare. This research proposes a novel framework that integrates double machine learning and targeted minimum loss-based estimation with Gaussian process regression to estimate treatment effects. The approach dynamically selects inducing points and model parameters based on the complexity of the data and the estimated treatment effects. We illustrate the application of our framework in the domain of medical testing where accurate estimation of treatment effects is crucial for assessing the efficacy of diagnostic tests and medical interventions. Through simulations and real-world data, we demonstrate the effectiveness of our adaptive approach in providing efficient estimates of treatment effects and improving decision-making. The research contributes to advancing the field of causal inference by introducing an adaptive approach that dynamically adjusts to the data characteristics, thereby addressing complex challenges in medical testing and intervention evaluation.

Causal inference

Artificial Intelligence (AI)

Double Machine Learning (DML)

Treatment effects

Adaptive

Inducing points 

Section on Nonparametric Statistics