6 citations · 25 across the 12 of their papers we have counts for
15 papers
Network-Constrained Matching and Randomization-Based Inference for Causal Effects
Zhejia Dong, Youjin Lee
Matching is widely used to mimic randomized experiments by forming matched sets in which treated and control units differ only randomly with respect to observed covariates. However…
Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods
Shuo Feng, Ishani Ganguli, Youjin Lee +3
Difference-in-differences (DiD) is the most popular observational causal inference method in health policy, employed to evaluate the real-world impact of policies and programs. To…
A Causal Framework for Evaluating Drivers of Policy Effect Heterogeneity Using Difference-in-Differences
Gary Hettinger, Youjin Lee, Nandita Mitra
Policymakers and researchers often seek to understand how a policy differentially affects a population and the pathways driving this heterogeneity. For example, when studying an ex…
Multiply Robust Difference-in-Differences Estimation of Causal Effect Curves for Continuous Exposures
Gary Hettinger, Youjin Lee, Nandita Mitra
Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interv…
Statistical and Causal Robustness for Causal Null Hypothesis Tests
Junhui Yang, Rohit Bhattacharya, Youjin Lee +1
Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model tha…
Policy effect evaluation under counterfactual neighborhood interventions in the presence of spillover
Youjin Lee, Gary Hettinger, Nandita Mitra
Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In re…
Estimation of Policy-Relevant Causal Effects in the Presence of Interference with an Application to the Philadelphia Beverage Tax
Gary Hettinger, Christina Roberto, Youjin Lee +1
To comprehensively evaluate a public policy intervention, researchers must consider the effects of the policy not just on the implementing region, but also nearby, indirectly-affec…
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
Taehyeon Koo, Youjin Lee, Dylan S. Small +1
We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It im…
Doubly Robust Nonparametric Instrumental Variable Estimators for Survival Outcomes
Youjin Lee, Edward H. Kennedy, Nandita Mitra
Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or conti…
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…
Partially Pooled Propensity Score Models for Average Treatment Effect Estimation with Multilevel Data
Youjin Lee, Trang Q. Nguyen, Elizabeth A. Stuart
Causal inference analyses often use existing observational data, which in many cases has some clustering of individuals. In this paper we discuss propensity score weighting methods…
Network Dependence Can Lead to Spurious Associations and Invalid Inference
Youjin Lee, Elizabeth L. Ogburn
Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a sma…
Causal inference, social networks, and chain graphs
Elizabeth L. Ogburn, Ilya Shpitser, Youjin Lee
Traditionally, statistical and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently t…
Testing for Network and Spatial Autocorrelation
Youjin Lee, Elizabeth L. Ogburn
Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties f…
Network Dependence Testing via Diffusion Maps and Distance-Based Correlations
Youjin Lee, Cencheng Shen, Carey E. Priebe +1
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high-dimensionality of n…