
A spectral adjustment for spatial confounding
Adjusting for an unmeasured confounder is generally an intractable probl...
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Multiply robust estimation of causal effects under principal ignorability
Causal inference concerns not only the average effect of the treatment o...
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Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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A Distributed Training Algorithm of Generative Adversarial Networks with Quantized Gradients
Training generative adversarial networks (GAN) in a distributed fashion ...
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Robust inference of conditional average treatment effects using dimension reduction
It is important to make robust inference of the conditional average trea...
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Improved Inference for Heterogeneous Treatment Effects Using RealWorld Data Subject to Hidden Confounding
The heterogeneity of treatment effect (HTE) lies at the heart of precisi...
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SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
With the increasing popularity of graphbased learning, Graph Neural Net...
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A review of spatial causal inference methods for environmental and epidemiological applications
The scientific rigor and computational methods of causal inference have ...
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SMIM: a unified framework of Survival sensitivity analysis using Multiple Imputation and Martingale
Censored survival data are common in clinical trial studies. We propose ...
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Generalized propensity score approach to causal inference with spatial interference
Many spatial phenomena exhibit treatment interference where treatments a...
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Spatially varying causal effect models
We establish causal effect models that allow for time and spatially var...
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Elastic Integrative Analysis of Randomized Trial and RealWorld Data for Treatment Heterogeneity Estimation
Parallel randomized trial (RT) and realworld (RW) data are becoming inc...
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A spatial causal analysis of wildland firecontributed PM2.5 using numerical model output
Wildland fire smoke contains hazardous levels of fine particulate matter...
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Integrative analysis of randomized clinical trials with real world evidence studies
We leverage the complementing features of randomized clinical trials (RC...
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Double score matching estimators of average and quantile treatment effects
Propensity score matching has a long tradition for handling confounding ...
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Statistical Data Integration in Survey Sampling: A Review
Finite population inference is a central goal in survey sampling. Probab...
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Causal inference of hazard ratio based on propensity score matching
Propensity score matching is commonly used to draw causal inference from...
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A Unified Framework for Causal Inference with Multiple Imputation Using Martingale
Multiple imputation is widely used to handle confounders missing at rand...
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A Tunably Compliant Origami Mechanism for Dynamically Dexterous Robots
We present an approach to overcoming challenges in dynamical dexterity f...
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Two Dimensional Router: Design and Implementation
Higher dimensional classification has attracted more attentions with inc...
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Multicause causal inference with unmeasured confounding and binary outcome
Unobserved confounding presents a major threat to causal inference in ob...
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Matrix Completion for Survey Data Prediction with Multivariate Missingness
Survey data are the goldstandard for estimating finite population param...
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Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
The problem of missingness in observational data is ubiquitous. When the...
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Doubly Robust Inference when Combining Probability and Nonprobability Samples with Highdimensional Data
Nonprobability samples become increasingly popular in survey statistics...
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Penetrating the Fog: the Path to Efficient CNN Models
With the increasing demand to deploy convolutional neural networks (CNNs...
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DomainAdversarial MultiTask Framework for Novel Therapeutic Property Prediction of Compounds
With the rapid development of highthroughput technologies, parallel acq...
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Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations
Structural Nested Mean Models (SNMMs) are useful for causal inference of...
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SECS: Efficient Deep Stream Processing via Class Skew Dichotomy
Despite that accelerating convolutional neural network (CNN) receives an...
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Semiparametric estimation of structural failure time model in continuoustime processes
Structural failure time models are causal models for estimating the effe...
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Integration of survey data and big observational data for finite population inference using mass imputation
Multiple data sources are becoming increasingly available for statistica...
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Combining multiple observational data sources to estimate causal effects
The era of big data has witnessed an increasing availability of multiple...
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Shu Yang
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