Introducing the At-Risk Average Causal Effect with Application to HealthWise South Africa

The Methodology Center, The Pennsylvania State University, 204 E. Calder Way, Ste. 400, State College, PA 16801, USA.
Prevention Science (Impact Factor: 2.63). 04/2012; 13(4):437-47. DOI: 10.1007/s11121-011-0271-0
Source: PubMed


Researchers often hypothesize that a causal variable, whether randomly assigned or not, has an effect on an outcome behavior and that this effect may vary across levels of initial risk of engaging in the outcome behavior. In this paper, we propose a method for quantifying initial risk status. We then illustrate the use of this risk-status variable as a moderator of the causal effect of leisure boredom, a non-randomized continuous variable, on cigarette smoking initiation. The data come from the HealthWise South Africa study. We define the causal effects using marginal structural models and estimate the causal effects using inverse propensity weights. Indeed, we found leisure boredom had a differential causal effect on smoking initiation across different risk statuses. The proposed method may be useful for prevention scientists evaluating causal effects that may vary across levels of initial risk.

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Available from: Linda L Caldwell, Sep 03, 2014
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    • "The issue of selection bias emerges with regard to the relation between changes in the mediator and changes in the outcome. As noted above, the mediator cannot be treated as though it has a causal influence on outcome, because the covariates that were balanced across treatments due to randomization could still bias the relation between the mediator and outcome (Coffman & Zhong, 2012). The bias evident in the link between mediators and outcomes is demonstrated in the following example: if changes in PTSD served as a mediator of treatment effects on SUDs in a conventional mediation analysis, inferences regarding the link between PTSD and SUDs could not be made as though clients had been randomized to different levels of change over time in PTSD (which, of course, can neither practically nor ethically be done). "

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    • "These weights work to create an " artificial RCT " where one could estimate causal effects as though both the treatment and the mediator were randomized. The weights for mediation would be calculated in a manner similar to the weights described in Section 3.2.2 for causal moderator effects (Coffman & Zhong, 2012). 3.2.4. "
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