Timezone: »

Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Ziyu Wang · Yuhao Zhou · Tongzheng Ren · Jun Zhu

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @ Virtual

Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking. In this work we present a scalable quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models. Contrary to Bayesian modeling for IV, our approach does not require additional assumptions on the data generating process, and leads to a scalable approximate inference algorithm with time cost comparable to the corresponding point estimation methods. Our algorithm can be further extended to work with neural network models. We analyze the theoretical properties of the proposed quasi-posterior, and demonstrate through empirical evaluation the competitive performance of our method.

Author Information

Ziyu Wang (Tsinghua University)
Yuhao Zhou (Tsinghua University)
Tongzheng Ren (UT Austin)
Jun Zhu (Tsinghua University)

More from the Same Authors