Explore the vast range of titles available.

Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects.We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart, under the assumption of no mediator–outcome confounders affected by exposure. In particular, identification of direct effects is guaranteed in experiments that randomize the exposure and the mediator.We propose various estimators of the direct and indirect effects, including substitution, reweighted and efficient estimators based on flexible regression techniques, allowing for multivariate mediators. Our efficient estimator is asymptotically linear under a condition requiring n 1/4-consistency of certain regression functions. We perform a simulation study in which we assess the finite sample properties of our proposed estimators.We present the results of an illustrative study where we assess the effect of participation in a sports team on the body mass index among children, using mediators such as exercise habits, daily consumption of snacks and overweight status.

We consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time varying confounders affected by prior exposure and a mediator, natural direct and indirect effects are not identified. However, we define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the 'mediational g-formula'. When there is no mediation, the mediational g-formula reduces to Robins's regular g-formula for longitudinal data. When there are no time varying confounders affected by prior exposure and mediator values, then the mediational g-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediational g-formula itself can accommodate both mediation and time varying confounders and constitutes a general approach to mediation analysis with time varying exposures and mediators.

We derive nonparametric bounds for local average treatment effects (LATE) without imposing the exclusion restriction assumption or requiring an outcome with bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential and counterfactual outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. The key element in our derivation is a result relating LATE to a causal mediation effect, which allows us to exploit partial identification results from the causal mediation analysis literature. The bounds are employed to analyze the effect of attaining a GED, high school, or vocational degree on future labor market outcomes using randomization into a training program as an invalid instrument. The resulting bounds are informative, indicating that the local effect when assigned to training for those whose degree attainment is affected by the instrument is at most 12.7 percentage points on employment and $64.4 on weekly earnings.

Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. In a first step, treatment propensity scores given the mediator and observed covariates or given covariates alone are estimated by nonparametric series logit estimation. In a second step, they are used to reweigh observations in order to estimate the effects of interest. We establish root-n consistency and asymptotic normality of this approach as well as a weighted version thereof. The latter allows evaluating effects on specific subgroups like the treated, for which we derive the asymptotic properties under estimated propensity scores. We also provide a simulation study and an application to an information intervention about male circumcisions.