5 papers
Parallel Trends as a Spectrum
Kyle Schindl, Joseph Fry, Florian Gunsilius +1
Difference-in-differences (DiD) designs are typically used to identify and estimate average causal effects, even though treatment can affect the entire outcome distribution. This h…
Distributional Discontinuity Design
Kyle Schindl, Larry Wasserman
Regression discontinuity and kink designs are typically analyzed through mean effects, even when treatment changes the shape of the entire outcome distribution. To address this, we…
Causal Geodesy: Counterfactual Estimation Along the Path Between Correlation and Causation
Kyle Schindl, Larry Wasserman
We introduce causal geodesy, a framework for studying the landscape of stochastic interventions that lie between the two extremes of performing no intervention and performing a sha…
Incremental effects for continuous exposures
Kyle Schindl, Shuying Shen, Edward H. Kennedy
Causal inference problems often involve continuous treatments, such as dose, duration, or frequency. However, identifying and estimating standard dose-response estimands requires t…
A Unified Framework for Rerandomization using Quadratic Forms
Kyle Schindl, Zach Branson
When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified lev…