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Found 7 papers, 5 papers with code
Optimizing Heat Alert Issuance with Reinforcement Learning
First, we introduce a novel RL environment enabling the evaluation of the effectiveness of heat alert policies to reduce heat-related hospitalizations.
SpaCE: The Spatial Confounding Environment
Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibly leading to spurious associations.
Causal Estimation of Exposure Shifts with Neural Networks: Evaluating the Health Benefits of Stricter Air Quality Standards in the US
Motivated by a key policy-relevant question in public health, we develop a neural network method and its theoretical underpinnings to estimate SRFs with robustness and efficiency guarantees.
Targeted active learning for probabilistic models
A fundamental task in science is to design experiments that yield valuable insights about the system under study.
Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units.
Adversarial Intrinsic Motivation for Reinforcement Learning
In this paper, we investigate whether one such objective, the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution, can be utilized effectively for reinforcement learning (RL) tasks.
Random clique covers for graphs with local density and global sparsity
Large real-world graphs tend to be sparse, but they often contain densely connected subgraphs and exhibit high clustering coefficients.
Methodology
