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- W4386655763 abstract "We consider estimation and inference of the effects of a policy in the absence of a control group. We obtain unbiased estimators of individual (heterogeneous) treatment effects and a consistent and asymptotically normal estimator of the average treatment effects, based on forecasting counterfactuals using a short time series of pre-treatment data. We show that the focus should be on forecast unbiasedness rather than accuracy. Correct specification of the forecasting model is not necessary to obtain unbiased estimates of individual treatment effects. Instead, simple basis function (e.g., polynomial time trends) regressions deliver unbiasedness under a broad class of data-generating processes for the individual counterfactuals. Basing the forecasts on a model can introduce misspecification bias and does not necessarily improve performance even under correct specification. Consistency and asymptotic normality of our Forecasted Average Treatment effects (FAT) estimator are attained under an additional assumption that rules out common and unforecastable shocks occurring between the treatment date and the date at which the effect is calculated." @default.
- W4386655763 created "2023-09-13" @default.
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- W4386655763 date "2023-09-11" @default.
- W4386655763 modified "2023-09-30" @default.
- W4386655763 title "Forecasted Treatment Effects" @default.
- W4386655763 doi "https://doi.org/10.48550/arxiv.2309.05639" @default.
- W4386655763 hasPublicationYear "2023" @default.
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