8 papers
Treatment effect estimation under convergent network interference
Bryan Park, Stefan Wager
Under network interference, a unit's observed outcome depends on the treatment assignment of its neighboring units in an exposure graph. Existing design-based asymptotic theory typ…
What is the Long-Term Value of Reliability?
Chenyu Qiu, Xu Kuang, Inessa Liskovich +2
We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer in…
Optimal Targeting in Dynamic Systems
Yuchen Hu, Shuangning Li, Stefan Wager
Modern treatment targeting methods often rely on estimating a conditional average treatment effect (CATE) using machine learning tools. While effective in identifying who benefits…
Non-parametric Causal Inference in Dynamic Thresholding Designs
Aditya Ghosh, Stefan Wager
We consider causal inference in dynamic settings where treatment is assigned by thresholding a state variable that can change over time. There is a large literature on regression-d…
Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference
Bryan Park, Stefan Wager
We propose a framework, the Neyman Jackknife, for conservative variance estimation in finite-population causal inference under interference. Our approach provides a general, flexib…
Sequentially-Rerandomized Switchback Experiments
Zhenghao Zeng, Christopher Adjaho, Alonso Bucarey +5
Large-scale online platforms and marketplace systems often evaluate new policies through experiments that randomize treatment across operational units (e.g., geographies, regions,…