Deep causal learning for robotic intelligence
Y Li - Frontiers in Neurorobotics, 2023 - frontiersin.org
This invited Review discusses causal learning in the context of robotic intelligence. The
Review introduces the psychological findings on causal learning in human cognition, as well …
Review introduces the psychological findings on causal learning in human cognition, as well …
EDVAE: Disentangled latent factors models in counterfactual reasoning for individual treatment effects estimation
Estimating individual treatment effect (ITE) from observational data is a crucial but
challenging task. Disentangled representations have been used to separate proxy variables …
challenging task. Disentangled representations have been used to separate proxy variables …
Targeted VAE: Structured inference and targeted learning for causal parameter estimation
Undertaking causal inference with observational data is extremely useful across a wide
range of domains including the development of medical treatments, advertisements and …
range of domains including the development of medical treatments, advertisements and …
Treatment Effects Estimation on Networked Observational Data using Disentangled Variational Graph Autoencoder
D Fan, R Jiang, Y Wen, C Gao - arXiv preprint arXiv:2412.14497, 2024 - arxiv.org
Estimating individual treatment effect (ITE) from observational data has gained increasing
attention across various domains, with a key challenge being the identification of latent …
attention across various domains, with a key challenge being the identification of latent …
Targeted VAE: Variational and targeted learning for causal inference
Undertaking causal inference with observational data is incredibly useful across a wide
range of tasks including the development of medical treatments, advertisements and …
range of tasks including the development of medical treatments, advertisements and …
A survey of deep causal models and their industrial applications
The notion of causality assumes a paramount position within the realm of human cognition.
Over the past few decades, there has been significant advancement in the domain of causal …
Over the past few decades, there has been significant advancement in the domain of causal …
Hi-ci: Deep causal inference in high dimensions
We address the problem of counterfactual regression using causal inference (CI) in obser-
vational studies consisting of high dimensional covariates and high cardinality treatments …
vational studies consisting of high dimensional covariates and high cardinality treatments …
Targeted vae: Variational and targeted learning for causal inference
Undertaking causal inference with observational data is incredibly useful across a wide
range of tasks including the development of medical treatments, advertisements and …
range of tasks including the development of medical treatments, advertisements and …
Counterfactual reasoning in observational studies
N Hassanpour - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
To identify the appropriate action to take, an intelligent agent must infer the causal effects of
every possible action choices. A prominent example is precision medicine that attempts to …
every possible action choices. A prominent example is precision medicine that attempts to …
[PDF][PDF] Inference of subgroup-level treatment effects via generic causal tree in observational studies. JUSTC, 2023, 53 (11): 1102. DOI: 10.52396
CW Zhang, ZM Zheng - 2015 - just.ustc.edu.cn
Exploring heterogeneity in causal effects has wide applications in the field of policy
evaluation and decisionmaking. In recent years, researchers have begun employing …
evaluation and decisionmaking. In recent years, researchers have begun employing …