Search Results for author: Ruoxuan Xiong

Found 13 papers, 5 papers with code

Instrumental Variables in Causal Inference and Machine Learning: A Survey

1 code implementation12 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.

Causal Inference

Confounder Balancing for Instrumental Variable Regression with Latent Variable

no code implementations18 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.

regression

Learning Individual Treatment Effects under Heterogeneous Interference in Networks

no code implementations25 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).

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

1 code implementation23 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).

regression

Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization

no code implementations4 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.

Domain Generalization

Federated Causal Inference in Heterogeneous Observational Data

1 code implementation25 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.

Causal Inference

Stable Prediction with Model Misspecification and Agnostic Distribution Shift

no code implementations31 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.

regression

Optimal Experimental Design for Staggered Rollouts

1 code implementation9 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.

Decision Making Experimental Design

Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference

no code implementations18 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.

Causal Inference

A tractable ellipsoidal approximation for voltage regulation problems

no code implementations9 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.

BIG-bench Machine Learning

Stable Prediction across Unknown Environments

no code implementations16 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.

Deep Learning Stock Volatility with Google Domestic Trends

2 code implementations15 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

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