17 citations · 36 across the 11 of their papers we have counts for
11 papers
Differential recall bias in estimating treatment effects in observational studies
Suhwan Bong, Kwonsang Lee, Francesca Dominici
Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to…
Covariate balancing using the integral probability metric for causal inference
Insung Kong, Yuha Park, Joonhyuk Jung +2
Weighting methods in causal inference have been widely used to achieve a desirable level of covariate balancing. However, the existing weighting methods have desirable theoretical…
Accounting for recall bias in case-control studies: a causal inference approach
Kwonsang Lee, Francesca Dominici
A case-control study is designed to help determine if an exposure is associated with an outcome. However, since case-control studies are retrospective, they are often subject to re…
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
Falco J. Bargagli-Stoffi, Riccardo Cadei, Kwonsang Lee +1
In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect…
Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes
Colin B. Fogarty, Kwonsang Lee, Rachel R. Kelz +1
We investigate the efficacy of surgical versus non-surgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instr…
Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models
Kwonsang Lee, Scott A. Lorch, Dylan S. Small
Two problems that arise in making causal inferences for non-mortality outcomes such as bronchopulmonary dysplasia (BPD) are unmeasured confounding and censoring by death, i.e., the…
Discovering Effect Modification and Randomization Inference in Air Pollution Studies
Kwonsang Lee, Dylan S. Small, Francesca Dominici
Studies have shown that exposure to air pollution, even at low levels, significantly increases mortality. As regulatory actions are becoming prohibitively expensive, robust evidenc…
A powerful approach to the study of moderate effect modification in observational studies
Kwonsang Lee, Dylan S. Small, Paul R. Rosenbaum
Effect modification means the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are in…
Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error
Kwonsang Lee, Dylan S. Small
Malaria is a parasitic disease that is a major health problem in many tropical regions. The most characteristic symptom of malaria is fever. The fraction of fevers that are attribu…
Discovering Effect Modification in an Observational Study of Surgical Mortality at Hospitals with Superior Nursing
Kwonsang Lee, Dylan S. Small, Jesse Y. Hsu +2
There is effect modification if the magnitude or stability of a treatment effect varies systematically with the level of an observed covariate. A larger or more stable treatment ef…
A Nonparametric Likelihood Approach for Inference in Instrumental Variable Models
Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin +1
Instrumental variable methods allow for inference about the treatment effect by controlling for unmeasured confounding in randomized experiments with noncompliance. However, many s…