12 papers
Non-overlap Average Treatment Effect Bounds
Herbert P. Susmann, Alec McClean, Iván DÃaz
The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have…
Comparing causal parameters with many treatments and positivity violations
Alec McClean, Yiting Li, Sunjae Bae +3
Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each ta…
Computationally and statistically efficient estimation of time-smoothed counterfactual curves
Herbert P. Susmann, Nicholas T. Williams, Richard Liu +2
Longitudinal causal inference is concerned with defining, identifying, and estimating the effect of a time-varying intervention on a time-varying outcome that is indexed by a follo…
Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Sean McGrath, Takuya Kawahara, Joshua Petimar +4
Researchers are often interested in estimating effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interes…
Identification and estimation of mediational effects of longitudinal modified treatment policies
Brian Gilbert, Katherine L. Hoffman, Nicholas Williams +3
We demonstrate a comprehensive semiparametric approach to causal mediation analysis, addressing the complexities inherent in settings with longitudinal and continuous treatments, c…
Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
Alec McClean, Iván DÃaz
When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are impossible to obs…