841 citations · 1.2k across the 34 of their papers we have counts for
14 papers · 1 filter
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
Yuchen Zhu, Limor Gultchin, Arthur Gretton +2
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setti…
Causal Machine Learning: A Survey and Open Problems
Jean Kaddour, Aengus Lynch, Qi Liu +2
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM). This perspective…
Operationalizing Complex Causes: A Pragmatic View of Mediation
Limor Gultchin, David S. Watson, Matt J. Kusner +1
We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not availa…
Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu +2
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e.g., graphs, images, texts). Given a weak condition on the effect, we propose…
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin +5
We address the problem of causal effect estimation in the presence of unobserved confounding, but where proxies for the latent confounder(s) are observed. We propose two kernel-bas…
A Class of Algorithms for General Instrumental Variable Models
Niki Kilbertus, Matt J. Kusner, Ricardo Silva
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making. There has been a flur…