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04/20/2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Estimating heterogeneous treatment effects from observational data is a ...
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11/28/2022
Malign Overfitting: Interpolation Can Provably Preclude Invariance
Learned classifiers should often possess certain invariance properties m...
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05/30/2022
Reinforcement Learning with a Terminator
We present the problem of reinforcement learning with exogenous terminat...
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04/21/2022
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Estimating the effects of continuous-valued interventions from observati...
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11/03/2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Estimating personalized treatment effects from high-dimensional observat...
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10/13/2021
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
We consider the problem of using expert data with unobserved confounders...
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03/08/2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
We study the problem of learning conditional average treatment effects (...
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02/20/2021
On Calibration and Out-of-domain Generalization
Out-of-domain (OOD) generalization is a significant challenge for machin...
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02/16/2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
We propose to analyse the conditional distributional treatment effect (C...
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08/25/2020
Using Deep Networks for Scientific Discovery in Physiological Signals
Deep neural networks (DNN) have shown remarkable success in the classifi...
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07/01/2020
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
Recommending the best course of action for an individual is a major appl...
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06/25/2020
A causal view of compositional zero-shot recognition
People easily recognize new visual categories that are new combinations ...
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06/11/2020
Bandits with Partially Observable Offline Data
We study linear contextual bandits with access to a large, partially obs...
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05/27/2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously di...
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03/24/2020
Generative ODE Modeling with Known Unknowns
In several crucial applications, domain knowledge is encoded by a system...
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01/21/2020
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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10/01/2019
Robust learning with the Hilbert-Schmidt independence criterion
We investigate the use of a non-parametric independence measure, the Hil...
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09/09/2019
Off-Policy Evaluation in Partially Observable Environments
This work studies the problem of batch off-policy evaluation for Reinfor...
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07/16/2019
Explaining Classifiers with Causal Concept Effect (CaCE)
How can we understand classification decisions made by deep neural nets?...
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10/27/2018
Removing Hidden Confounding by Experimental Grounding
Observational data is increasingly used as a means for making individual...
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10/19/2018
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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02/23/2018
Learning Weighted Representations for Generalization Across Designs
Predictive models that generalize well under distributional shift are of...
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07/09/2017
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Statisticians have made great strides towards assumption-free estimation...
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05/24/2017
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such a...
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09/30/2016
Structured Inference Networks for Nonlinear State Space Models
Gaussian state space models have been used for decades as generative mod...
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06/13/2016
Estimating individual treatment effect: generalization bounds and algorithms
There is intense interest in applying machine learning to problems of ca...
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05/12/2016
Learning Representations for Counterfactual Inference
Observational studies are rising in importance due to the widespread acc...
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11/16/2015
Deep Kalman Filters
Kalman Filters are one of the most influential models of time-varying ph...
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12/02/2013