• Corpus ID: 244116793

Causal effect on a target population: a sensitivity analysis to handle missing covariates

@inproceedings{Colnet2021CausalEO,
  title={Causal effect on a target population: a sensitivity analysis to handle missing covariates},
  author={B'en'edicte Colnet and Julie Josse and Erwan Scornet and Ga{\"e}l Varoquaux},
  year={2021}
}
Randomized Controlled Trials (RCTs) are often considered as the gold standard to conclude on the causal effect of a given intervention on an outcome, but they may lack of external validity when the population eligible to the RCT is substantially different from the target population. Having at hand a sample of the target population of interest allows to generalize the causal effect. Identifying this target population treatment effect needs covariates in both sets to capture all treatment effect modi… 
Causal inference methods for combining randomized trials and observational studies: a review
TLDR
This paper first discusses identification and estimation methods that improve generalizability of randomized controlled trials (RCTs) using the representativeness of observational data, and methods that combining RCTs and observational data to improve the (conditional) average treatment effect estimation.

References

SHOWING 1-10 OF 79 REFERENCES
A Review of Generalizability and Transportability
When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and
Causal inference methods for combining randomized trials and observational studies: a review
TLDR
This paper first discusses identification and estimation methods that improve generalizability of randomized controlled trials (RCTs) using the representativeness of observational data, and methods that combining RCTs and observational data to improve the (conditional) average treatment effect estimation.
Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects
TLDR
This paper considers sensitivity analyses for two situations: (1) where the authors cannot adjust for a specific moderator $V$ observed in the RCT because they do not observe it in the target population; and (2) where they are concerned that the treatment effect may be moderated by factors not observed even in theRCT, which they represent as a composite moderator $U$.
Generalizing causal inferences from randomized trials: counterfactual and graphical identification
When engagement with a randomized trial is driven by factors that affect the outcome or when trial engagement directly affects the outcome independent of treatment, the average treatment effect among
Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials
Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly
Making sense of sensitivity: extending omitted variable bias
TLDR
The omitted variable bias framework is extended with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounds, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results.
Extending inferences from a randomized trial to a new target population
TLDR
This tutorial considers methods for extending causal inferences about time‐fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariates from a sample from the target population.
Extending inferences from a randomized trial to a target population
TLDR
This work presents simple methods for sensitivity analyses that directly parameterize violations of ``generalizability'' or ``transportability'' assumptions using bias functions, and illustrates the methods using data from a clinical trial comparing treatments for chronic hepatitis C infection.
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
TLDR
Austen plots are developed, a sensitivity analysis tool to aid domain experts' judgments about whether strong confounders in an observational study are plausible, by making it easier to reason about potential bias induced by unobserved confounding.
Covariate selection for generalizing experimental results: Application to a large‐scale development program in Uganda *
  • Naoki Egami, E. Hartman
  • Economics
    Journal of the Royal Statistical Society: Series A (Statistics in Society)
  • 2021
TLDR
This article proposes a method to estimate a separating set -- a set of variables affecting both the sampling mechanism and treatment effect heterogeneity -- and shows that the population average treatment effect (PATE) can be identified by adjusting for estimated separating sets.
...
...