# 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…

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Causal inference methods for combining randomized trials and observational studies: a review

- Economics
- 2020

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.

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