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Model selection for estimation of causal parameters

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If you have a question about this talk, please contact Dr Sergio Bacallado.

In causal inference, the goal is often to estimate average treatment effects. Selecting a model by cross-validation in this context can be problematic, as models that exhibit great predictive accuracy can be suboptimal for estimating the parameter of interest. We discuss several approaches to perform model selection in this context and compare their performance on simulated data sets.

This talk is part of the Statistics series.

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