… subclassification splines (MITSS) that explicitly views causal effect estimation as a missing
data problem 13, 14 but only considered binary outcomes and a scalar covariate. Here, we …

… In conclusion, this manuscript suggests … estimating causal effects on a dichotomous outcome
that is relatively efficient as well. The procedure also allows the estimation of causal effects …

… regression to estimate causal effects of any variables with any distribution on binary outcomes…
Third, I introduce the framework of potential outcomes through the Neyman-Rubin causal …

… First, we extend BART to the multiple treatment and binary outcome setting, highlighting
that the strengths of BART for binary treatment also manifest with multiple treatments. Second, …

… With a binary outcome Y, we can write a “threshold model” for … to estimate unconditional
average causal effects and/or effects … average causal effects, only local average causal effects.” …

… Alternatively, researchers have also used models designed for binary outcomes to estimate
R(X), including logistic and probit re gression models on different subsets of subjects re …

… We provide sufficient conditions for estimating from longitudinal data the causal effect of a …
of response for a dichotomous outcome. We then show how one can estimate this effect under …

… we consider the estimation of causal effects using SMMs from studies with binary outcomes.
… conditions under which each SMM estimator identifies its target causal parameter, and the …

… analysing the causal effect of actual exposure to drug treatment on a (repeated) binary outcome
in … for ordinal compliance and monotone dose response, proposed by Goetghebeur and …

… This manuscript compares previously suggested procedures for estimating causal effects
when there is a key covariate that is unbalanced between treatment groups. The simulations …