4 papers
Model Risk in Machine-Learning Distributional IV Estimation
Charles Shaw
We study model risk in machine-learning estimation of the Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE), the distributional IV effect for the subp…
A Path Signature Framework for Detecting Creative Fatigue in Digital Advertising
Charles Shaw
This paper introduces a signature-based framework for detecting advertising creative fatigue using path signatures, a geometric representation from rough path theory. Creative fati…
srvar-toolkit: A Python Implementation of Shadow-Rate Vector Autoregressions with Stochastic Volatility
Charles Shaw
We introduce srvar-toolkit, an open-source Python package for Bayesian vector autoregression with shadow-rate constraints and stochastic volatility. The toolkit implements the meth…
Optimized Supergeo Design: A Scalable Framework for Geographic Marketing Experiments
Charles Shaw
Geographic experiments are a widely-used methodology for measuring incremental return on ad spend (iROAS) at scale, yet their design presents significant challenges. The unit count…