Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity
John M. Brooks,
Cole G. Chapman,
Brian K. Chen
et al.
Abstract:Background
Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice – essential heterogeneity.
Methods
We simulated eleven populations with identical treatment effect distributions based on patient factors. The populations vari… Show more
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