Publications (14)
Instrumental Variable Estimation When Compliance is not Deterministic: The Stochastic Monotonicity Assumption
Dylan Small, Zhiqiang Tan, Scott Lorch +1
The instrumental variables (IV) method is a method for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confou…
Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome
Marshall M. Joffe, Dylan Small, Chi-Yuan Hsu
It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made.…
A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in U.S. children
Bo Zhang, Dylan Small
Matched observational studies are commonly used to study treatment effects in non-randomized data. After matching for observed confounders, there could remain bias from unobserved…
Sensitivity Analysis for Matched Observational Studies with Continuous Exposures and Binary Outcomes
Jeffrey Zhang, Dylan Small, Siyu Heng
Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be remov…
Pre-analysis protocol for an observational study on the effects of adolescent sports participation on health in early adulthood
Ajinkya H Kokandakar, Yuzhou Lin, Steven Jin +5
We will study the impact of adolescent sports participation on early-adulthood health using longitudinal data from the National Study of Youth and Religion. We focus on two primary…
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models
Zijian Guo, Dylan Small
The instrumental variable method consistently estimates the effect of a treatment when there is unmeasured confounding and a valid instrumental variable. A valid instrumental varia…