5,390 research outputs found
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Treatment effect estimation in continuous time is crucial for personalized
medicine. However, existing methods for this task are limited to point
estimates of the potential outcomes, whereas uncertainty estimates have been
ignored. Needless to say, uncertainty quantification is crucial for reliable
decision-making in medical applications. To fill this gap, we propose a novel
Bayesian neural controlled differential equation (BNCDE) for treatment effect
estimation in continuous time. In our BNCDE, the time dimension is modeled
through a coupled system of neural controlled differential equations and neural
stochastic differential equations, where the neural stochastic differential
equations allow for tractable variational Bayesian inference. Thereby, for an
assigned sequence of treatments, our BNCDE provides meaningful posterior
predictive distributions of the potential outcomes. To the best of our
knowledge, ours is the first tailored neural method to provide uncertainty
estimates of treatment effects in continuous time. As such, our method is of
direct practical value for promoting reliable decision-making in medicine
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
Reliable Off-Policy Learning for Dosage Combinations
Decision-making in personalized medicine such as cancer therapy or critical
care must often make choices for dosage combinations, i.e., multiple continuous
treatments. Existing work for this task has modeled the effect of multiple
treatments independently, while estimating the joint effect has received little
attention but comes with non-trivial challenges. In this paper, we propose a
novel method for reliable off-policy learning for dosage combinations. Our
method proceeds along three steps: (1) We develop a tailored neural network
that estimates the individualized dose-response function while accounting for
the joint effect of multiple dependent dosages. (2) We estimate the generalized
propensity score using conditional normalizing flows in order to detect regions
with limited overlap in the shared covariate-treatment space. (3) We present a
gradient-based learning algorithm to find the optimal, individualized dosage
combinations. Here, we ensure reliable estimation of the policy value by
avoiding regions with limited overlap. We finally perform an extensive
evaluation of our method to show its effectiveness. To the best of our
knowledge, ours is the first work to provide a method for reliable off-policy
learning for optimal dosage combinations.Comment: Accepted at NeurIPS 202
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