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Continuous Treatment Effects with Surrogate Outcomes
In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem.
Temporal Aggregation for the Synthetic Control Method
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data.
Using Multiple Outcomes to Improve the Synthetic Control Method
When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome.
Augmented balancing weights as linear regression
These popular doubly robust or double machine learning estimators combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score.
Outcome Assumptions and Duality Theory for Balancing Weights
We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population.
