7 papers
Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length
Mengchu Zheng, Matteo Bonvini, Zijian Guo
We study inference on a low-dimensional functional in the presence of infinite-dimensional nuisance parameters. Classical inferential methods are typically based on Wald inter…
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learni…
Causal Invariance Learning via Efficient Nonconvex Optimization
Zhenyu Wang, Yifan Hu, Peter Bühlmann +1
Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome a…
Synthetic Control with Weight Uncertainty: Robust Identification and Statistical Inference
Taehyeon Koo, Zijian Guo
The synthetic control method estimates causal effects by comparing a treated unit with weighted controls matched on its pre-treatment trajectory. However, validity can be compromis…
Post-selection inference for causal effects after causal discovery
Ting-Hsuan Chang, Zijian Guo, Daniel Malinsky
Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of…
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Yihong Gu, Cong Fang, Yang Xu +2
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and…