Search Results for author: Raha Moraffah

Found 7 papers, 0 papers with code

Evaluation Methods and Measures for Causal Learning Algorithms

no code implementations7 Feb 2022 Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K. Selcuk Candan, Huan Liu

To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.

Causal Inference

Causal Inference for Time series Analysis: Problems, Methods and Evaluation

no code implementations11 Feb 2021 Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, Huan Liu

In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.

Causal Discovery Causal Inference +2

Use of Bayesian Nonparametric methods for Estimating the Measurements in High Clutter

no code implementations30 Nov 2020 Bahman Moraffah, Christ Richmond, Raha Moraffah, Antonia Papandreou-Suppappola

We robustly and accurately estimate the trajectory of the moving target in a high clutter environment with an unknown number of clutters by employing Bayesian nonparametric modeling.

Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation

no code implementations9 Mar 2020 Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu

In this work, models that aim to answer causal questions are referred to as causal interpretable models.

Decision Making

Deep causal representation learning for unsupervised domain adaptation

no code implementations28 Oct 2019 Raha Moraffah, Kai Shu, Adrienne Raglin, Huan Liu

Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.

Representation Learning Unsupervised Domain Adaptation

Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects

no code implementations9 Aug 2018 Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.

Variational Inference

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