Afsaneh Mastouri — accepted AI-conference papers

2024

Bounding causal effects with leaky instruments

David Watson, Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva

UAI 2024poster

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the

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2021

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt Kusner +2

ICML 2021spotlight

We address the problem of causal effect estima-tion in the presence of unobserved confounding,but where proxies for the latent confounder(s) areobserved. We propose two kernel-based meth-ods for nonlinear causal effect estimation in thissetting: (a) a two-stage regression approach, and(b) a maximum…

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