294 citations · 1.2k across the 73 of their papers we have counts for
11 papers · 1 filter
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Nathan Drenkow, Numair Sani, Ilya Shpitser +1
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-impactful applications, motivating the need to close the gap in model performance…
Causal Inference with Hidden Mediators
AmirEmad Ghassami, Alan Yang, Ilya Shpitser +1
Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are availa…
Partially Intervenable Causal Models
AmirEmad Ghassami, Ilya Shpitser
Graphical causal models led to the development of complete non-parametric identification theory in arbitrary structured systems, and general approaches to efficient inference. Neve…
An Automated Approach to Causal Inference in Discrete Settings
Guilherme Duarte, Noam Finkelstein, Dean Knox +2
When causal quantities cannot be point identified, researchers often pursue partial identification to quantify the range of possible values. However, the peculiarities of applied r…
Proximal mediation analysis
Oliver Dukes, Ilya Shpitser, Eric J. Tchetgen Tchetgen
A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In…
The Proximal ID Algorithm
Ilya Shpitser, Zach Wood-Doughty, Eric J. Tchetgen Tchetgen
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to addr…