… the statistical counterpart of a causal parameter. In this article a new targeted maximum
likelihood estimator for estimating a causal effect of a binary treatment on a continuous outcome …

… targeted maximum likelihood estimator of causal effects of … clinical outcome, and missingness
of the clinical outcome. … Lower bound for the variance of an unbiased estimator is maximal …

… into the estimate. We present an alternative targeted maximum likelihood estimation (TMLE)
approach that dampens the effect of these heavily weighted observations. As a substitution …

… define formally the natural direct causal effect of a binary treatment on an outcome using the
Non… has a version with a binary outcome Y and a version with a continuous and bounded …

… Doubly robust estimation in missing data and causal inference models. Biometrics, 61:962–…
targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome. …

… applications of causal effect estimation use … outcome is bounded and continuous,
within the TMLE algorithm we transform Y to be bounded within (0, 1) and use a quasi-log-likelihood …

… semi-parametric substitution estimator of a causal effect parameter or statistical association
… log likelihood for binary outcomes is a valid loss function for continuous outcomes bounded …

… of a marginal causal effect, and the parametric component in a … to hold if the log likelihood
of the data is uniformly bounded … causal effect of treatment A on the outcome Y . In this case, Ψ(…

… The outcome variable can either be continuous or binary. Assume we are interested in estimating
the marginal additive causal effect of treatment on the outcome. … -Rao efficiency bound. …

… of the additive causal effect of a binary treatment on a bounded continuous outcome, which
… study we will use simple parametric maximum likelihood estimators as initial estimators Q0 …