53 citations · 172 across the 26 of their papers we have counts for
26 papers
Bayesian Causal Forests & the 2022 ACIC Data Challenge: Scalability and Sensitivity
Ajinkya H. Kokandakar, Hyunseung Kang, Sameer K. Deshpande
We demonstrate how Hahn et al.'s Bayesian Causal Forests model (BCF) can be used to estimate conditional average treatment effects for the longitudinal dataset in the 2022 American…
Propensity Score Modeling: Key Challenges When Moving Beyond the No-Interference Assumption
Hyunseung Kang, Chan Park, Ralph Trane
The paper presents some models for the propensity score. Considerable attention is given to a recently popular, but relatively under-explored setting in causal inference where the…
Semiparametric Efficient Dimension Reduction in multivariate regression with an Inner Envelope
Linquan Ma, Hyunseung Kang, Lan Liu
Recently, Su and Cook proposed a dimension reduction technique called the inner envelope which can be substantially more efficient than the original envelope or existing dimension…
A Robust, Differentially Private Randomized Experiment for Evaluating Online Educational Programs With Sensitive Student Data
Manjusha Kancharla, Hyunseung Kang
Randomized control trials (RCTs) have been the gold standard to evaluate the effectiveness of a program, policy, or treatment on an outcome of interest. However, many RCTs assume t…
Minimum Resource Threshold Policy Under Partial Interference
Chan Park, Guanhua Chen, Menggang Yu +1
When developing policies for prevention of infectious diseases, policymakers often set specific, outcome-oriented targets to achieve. For example, when developing a vaccine allocat…
A More Efficient, Doubly Robust, Nonparametric Estimator of Treatment Effects in Multilevel Studies
Chan Park, Hyunseung Kang
When studying treatment effects in multilevel studies, investigators commonly use (semi-)parametric estimators, which make strong parametric assumptions about the outcome, the trea…
Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
Chan Park, Hyunseung Kang
Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on st…
Two Robust Tools for Inference about Causal Effects with Invalid Instruments
Hyunseung Kang, Youjin Lee, T. Tony Cai +1
Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental vari…
Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference
Chan Park, Hyunseung Kang
Recently, many estimators for network treatment effects have been proposed. But, their optimality properties in terms of semiparametric efficiency have yet to be resolved. We prese…
Inferring Treatment Effects After Testing Instrument Strength in Linear Models
Nan Bi, Hyunseung Kang, Jonathan Taylor
A common practice in IV studies is to check for instrument strength, i.e. its association to the treatment, with an F-test from regression. If the F-statistic is above some thresho…
ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable
Hyunseung Kang, Yang Jiang, Qingyuan Zhao +1
We present a comprehensive R software ivmodel for analyzing instrumental variables with one endogenous variable. The package implements a general class of estimators called k- clas…
Inference After Selecting Plausibly Valid Instruments with Application to Mendelian Randomization
Nan Bi, Hyunseung Kang, Jonathan Taylor
Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome by using genetic instruments. These instruments are oft…
Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization
Ting Ye, Jun Shao, Hyunseung Kang
Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challen…
Weak-Instrument Robust Tests in Two-Sample Summary-Data Mendelian Randomization
Sheng Wang, Hyunseung Kang
Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables…
A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference
Chan Park, Hyunseung Kang
Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often ha…
Detecting Heterogeneous Treatment Effect with Instrumental Variables
Michael Johnson, Jiongyi Cao, Hyunseung Kang
There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand het…
Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach
Siyu Heng, Hyunseung Kang, Dylan S. Small +1
In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is…
Estimation Methods for Cluster Randomized Trials with Noncompliance: A Study of A Biometric Smartcard Payment System in India
Hyunseung Kang, Luke Keele
Many policy evaluations occur in settings where treatment is randomized at the cluster level, and there is treatment noncompliance within each cluster. For example, villages might…
Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects
Hyunseung Kang, Guido Imbens
In non-network settings, encouragement designs have been widely used to analyze causal effects of a treatment, policy, or intervention on an outcome of interest when randomizing th…
Testing Endogeneity with High Dimensional Covariates
Zijian Guo, Hyunseung Kang, T. Tony Cai +1
Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous variables and putting lit…
Inference for Instrumental Variables: A Randomization Inference Approach
Hyunseung Kang, Laura Peck, Luke Keele
The method of instrumental variables (IV) provides a framework to study causal effects in both randomized experiments with noncompliance and in observational studies where natural…
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
Susan Athey, Raj Chetty, Guido Imbens +1
Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in t…
Confidence Intervals for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting
Zijian Guo, Hyunseung Kang, T. Tony Cai +1
A major challenge in instrumental variables (IV) analysis is to find instruments that are valid, or have no direct effect on the outcome and are ignorable. Typically one is unsure…
A simple and robust confidence interval for causal effects with possibly invalid instruments
Hyunseung Kang, T. Tony Cai, Dylan S. Small
Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental vari…