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…