We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure-outcome confounding.Funding Agencies|Swedish Research CouncilSwedish Research Counc...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
Unmeasured confounding is an important threat to the validity of observational studies. A common way...
Random-effects meta-analyses of observational studies can produce biased estimates if the synthesize...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
Unmeasured confounding may undermine the validity of causal inference with observational studies. Se...
It is often of interest to decompose the total effect of an exposure into a component that acts on t...
Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an...
It is often of interest to decompose the total effect of an exposure into a component that acts on t...
Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
Unmeasured confounding is one of the most important threats to the validity of observational studies...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
Unmeasured confounding is an important threat to the validity of observational studies. A common way...
Random-effects meta-analyses of observational studies can produce biased estimates if the synthesize...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
We present a method for assessing the sensitivity of the true causal effect to unmeasured confoundin...
Unmeasured confounding may undermine the validity of causal inference with observational studies. Se...
It is often of interest to decompose the total effect of an exposure into a component that acts on t...
Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an...
It is often of interest to decompose the total effect of an exposure into a component that acts on t...
Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
Unmeasured confounding is one of the most important threats to the validity of observational studies...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assu...
Unmeasured confounding is an important threat to the validity of observational studies. A common way...
Random-effects meta-analyses of observational studies can produce biased estimates if the synthesize...