281 citations · 1.2k across the 30 of their papers we have counts for
11 papers · 1 filter
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
Tomer Meir, Uri Shalit, Malka Gorfine
Tailoring treatments to individual needs is a central goal in fields such as medicine. A key step toward this goal is estimating Heterogeneous Treatment Effects (HTE) - the way tre…
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
Junhyung Park, Uri Shalit, Bernhard Schölkopf +1
We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to en…
Using Deep Networks for Scientific Discovery in Physiological Signals
Tom Beer, Bar Eini-Porat, Sebastian Goodfellow +2
Deep neural networks (DNN) have shown remarkable success in the classification of physiological signals. In this study we propose a method for examining to what extent does a DNN's…
Generative ODE Modeling with Known Unknowns
Ori Linial, Neta Ravid, Danny Eytan +1
In several crucial applications, domain knowledge is encoded by a system of ordinary differential equations (ODE), often stemming from underlying physical and biological processes.…
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from…
Learning Weighted Representations for Generalization Across Designs
Fredrik D. Johansson, Nathan Kallus, Uri Shalit +1
Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to building robust and reliable machine learning applications. We focus…
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for pol…
Structured Inference Networks for Nonlinear State Space Models
Rahul G. Krishnan, Uri Shalit, David Sontag
Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form,…
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D. Johansson, David Sontag
There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal…
Learning Representations for Counterfactual Inference
Fredrik D. Johansson, Uri Shalit, David Sontag
Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of a…
Deep Kalman Filters
Rahul G. Krishnan, Uri Shalit, David Sontag
Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy wide…