16 citations · 31 across the 5 of their papers we have counts for
7 papers
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami +5
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as c…
Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter
Bahman Moraffah, Christ Richmond, Raha Moraffah +1
Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters…
Causal Adversarial Network for Learning Conditional and Interventional Distributions
Raha Moraffah, Bahman Moraffah, Mansooreh Karami +2
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which re…
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo +2
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made b…
Deep causal representation learning for unsupervised domain adaptation
Raha Moraffah, Kai Shu, Adrienne Raglin +1
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data.…