54 citations · 85 across the 7 of their papers we have counts for
8 papers
Protocol for a Study of the Effect of Surface Mining in Central Appalachia on Adverse Birth Outcomes
Dylan S. Small, Dan Firth, Luke Keele +4
Surface mining has become a major method of coal mining in Central Appalachia alongside the traditional underground mining. Concerns have been raised about the health effects of th…
Re-Evaluating Strengthened-IV Designs: Asymptotic Efficiency, Bias Formula, and the Validity and Power of Sensitivity Analyses
Siyu Heng, Bo Zhang, Xu Han +2
Instrumental variables (IVs) are extensively used to estimate treatment effects when the treatment and outcome are confounded by unmeasured confounders; however, weak IVs are often…
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…
Robust causal inference with continuous instruments using the local instrumental variable curve
Edward H. Kennedy, Scott A. Lorch, Dylan S. Small
Instrumental variables are commonly used to estimate effects of a treatment afflicted by unmeasured confounding, and in practice instruments are often continuous (e.g., measures of…
Using an Instrumental Variable to Test for Unmeasured Confounding
Zijian Guo, Jing Cheng, Scott A. Lorch +1
An important concern in an observational study is whether or not there is unmeasured confounding, i.e., unmeasured ways in which the treatment and control groups differ before trea…
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…