Modeling and predicting of water production by capacitive deionization method using artificial neural networks
Water desalination is a method to deal with water shortage that today considered as a way
to meet the growing human demand. Obtaining freshwater at low cost guarantees the …
to meet the growing human demand. Obtaining freshwater at low cost guarantees the …
Causal imitability under context-specific independence relations
F Jamshidi, S Akbari… - Advances in Neural …, 2024 - proceedings.neurips.cc
Drawbacks of ignoring the causal mechanisms when performing imitation learning have
recently been acknowledged. Several approaches both to assess the feasibility of imitation …
recently been acknowledged. Several approaches both to assess the feasibility of imitation …
Revisiting the general identifiability problem
We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for
causal inference and note that it is necessary to add positivity assumption of observational …
causal inference and note that it is necessary to add positivity assumption of observational …
s-id: Causal effect identification in a sub-population
AM Abouei, E Mokhtarian, N Kiyavash - Proceedings of the AAAI …, 2024 - ojs.aaai.org
Causal inference in a sub-population involves identifying the causal effect of an intervention
on a specific subgroup, which is distinguished from the whole population through the …
on a specific subgroup, which is distinguished from the whole population through the …
[HTML][HTML] Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi
MT Filigheddu, M Leonelli, G Varando… - Computational and …, 2024 - Elsevier
Abstract Machine learning models are increasingly used in the medical domain to study the
association between risk factors and diseases to support practitioners in understanding …
association between risk factors and diseases to support practitioners in understanding …
On identifiability of conditional causal effects
We address the problem of identifiability of an arbitrary conditional causal effect given both
the causal graph and a set of any observational and/or interventional distributions of the …
the causal graph and a set of any observational and/or interventional distributions of the …
Causal Effect Identification in a Sub-Population with Latent Variables
AM Abouei, E Mokhtarian, N Kiyavash… - arxiv preprint arxiv …, 2024 - arxiv.org
The s-ID problem seeks to compute a causal effect in a specific sub-population from the
observational data pertaining to the same sub population (Abouei et al., 2023). This problem …
observational data pertaining to the same sub population (Abouei et al., 2023). This problem …
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust
H Babaei, S Alemohammad… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The first step toward investigating the effectiveness of a treatment via a randomized trial is to
split the population into control and treatment groups then compare the average response of …
split the population into control and treatment groups then compare the average response of …