
Quantifying Ignorance in IndividualLevel CausalEffect Estimates under Hidden Confounding
We study the problem of learning conditional average treatment effects (...
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On Calibration and Outofdomain Generalization
Outofdomain (OOD) generalization is a significant challenge for machin...
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Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and UStatistic Regression
We propose to analyse the conditional distributional treatment effect (C...
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Using Deep Networks for Scientific Discovery in Physiological Signals
Deep neural networks (DNN) have shown remarkable success in the classifi...
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Identifying Causal Effect Inference Failure with UncertaintyAware Models
Recommending the best course of action for an individual is a major appl...
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A causal view of compositional zeroshot recognition
People easily recognize new visual categories that are new combinations ...
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Bandits with Partially Observable Offline Data
We study linear contextual bandits with access to a large, partially obs...
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CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously di...
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Generative ODE Modeling with Known Unknowns
In several crucial applications, domain knowledge is encoded by a system...
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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
Practitioners in diverse fields such as healthcare, economics and educat...
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Robust learning with the HilbertSchmidt independence criterion
We investigate the use of a nonparametric independence measure, the Hil...
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OffPolicy Evaluation in Partially Observable Environments
This work studies the problem of batch offpolicy evaluation for Reinfor...
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Explaining Classifiers with Causal Concept Effect (CaCE)
How can we understand classification decisions made by deep neural nets?...
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Removing Hidden Confounding by Experimental Grounding
Observational data is increasingly used as a means for making individual...
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Harmonizing Fully Optimal Designs with Classic Randomization in Fixed Trial Experiments
There is a movement in design of experiments away from the classic rando...
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Learning Weighted Representations for Generalization Across Designs
Predictive models that generalize well under distributional shift are of...
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Automated versus doityourself methods for causal inference: Lessons learned from a data analysis competition
Statisticians have made great strides towards assumptionfree estimation...
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Causal Effect Inference with Deep LatentVariable Models
Learning individuallevel causal effects from observational data, such a...
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Structured Inference Networks for Nonlinear State Space Models
Gaussian state space models have been used for decades as generative mod...
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Estimating individual treatment effect: generalization bounds and algorithms
There is intense interest in applying machine learning to problems of ca...
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Learning Representations for Counterfactual Inference
Observational studies are rising in importance due to the widespread acc...
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Deep Kalman Filters
Kalman Filters are one of the most influential models of timevarying ph...
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Efficient coordinatedescent for orthogonal matrices through Givens rotations
Optimizing over the set of orthogonal matrices is a central component in...
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Uri Shalit
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