1 code implementation • 12 Dec 2022 • Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Fei Wu
This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning.
no code implementations • 18 Nov 2022 • Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.
no code implementations • 25 Oct 2022 • Ziyu Zhao, Kun Kuang, Ruoxuan Xiong, Fei Wu
In network data, due to interference, the outcome of a unit is influenced not only by its treatment (i. e., direct effects) but also by others' treatments (i. e., spillover effects).
1 code implementation • 23 Aug 2022 • Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).
no code implementations • 4 Oct 2021 • Junkun Yuan, Xu Ma, Kun Kuang, Ruoxuan Xiong, Mingming Gong, Lanfen Lin
Specifically, it first learns the conditional distribution of input features of one domain given input features of another domain, and then it estimates the domain-invariant relationship by predicting labels with the learned conditional distribution.
1 code implementation • 25 Jul 2021 • Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, Susan Athey
We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site.
no code implementations • 21 Apr 2020 • Allison Koenecke, Michael Powell, Ruoxuan Xiong, Zhu Shen, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Marco Trevisan, Pär Sparen, Juan J Carrero, Akihiko Nishimura, Brian Caffo, Elizabeth A. Stuart, Renyuan Bai, Verena Staedtke, David L. Thomas, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Shibin Zhou, Chetan Bettegowda, Maximilian F. Konig, Brett Mensh, Joshua T. Vogelstein, Susan Athey
Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18, 547) and three cohorts with pneumonia (n=400, 907).
no code implementations • 31 Jan 2020 • Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li
Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data.
1 code implementation • 9 Nov 2019 • Ruoxuan Xiong, Susan Athey, Mohsen Bayati, Guido Imbens
Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected.
no code implementations • 18 Oct 2019 • Ruoxuan Xiong, Markus Pelger
We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns.
no code implementations • 9 Mar 2019 • Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto Sangiovanni-Vincentelli, Baosen Zhang
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
no code implementations • 16 Jun 2018 • Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li
In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments.
2 code implementations • 15 Dec 2015 • Ruoxuan Xiong, Eric P. Nichols, Yuan Shen
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors.
Computational Finance