6 papers
Sensitivity Analysis of the Consistency Assumption
Brian Knaeble, Qinyun Lin, Erich Kummerfeld +1
Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden…
Multiple Regression Analysis of Unmeasured Confounding
Brian Knaeble, R Mitchell Hughes
Whereas confidence intervals are used to assess uncertainty due to unmeasured individuals, confounding intervals can be used to assess uncertainty due to unmeasured attributes. Pre…
Temporal discontinuity trials and randomization: success rates versus design strength
Brian Knaeble, Erich Kummerfeld
We consider the following comparative effectiveness scenario. There are two treatments for a particular medical condition: a randomized experiment has demonstrated mediocre effecti…
Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models
Brian Knaeble, R. Mitchell Hughes, George Rudolph +2
It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the…
Partial Identification of the Average Treatment Effect with Stochastic Counterfactuals and Discordant Twins
Brian Knaeble, Braxton Osting, Placede Tshiaba
We develop a novel approach to partially identify causal estimands, such as the average treatment effect (ATE), from observational data. To better satisfy the stable unit treatment…
Maximum Entropy Estimation of Heterogeneous Causal Effects
Brian Knaeble, Mehdi Hakim-Hashemi, Mark A. Abramson
For the purpose of causal inference we employ a stochastic model of the data generating process, utilizing individual propensity probabilities for the treatment, and also individua…