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Statistical Laboratory

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.

Frontpage talks

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06
Nov
16:00 - 17:00: Title to be confirmed
Statistics

Statistics

Further information

Time:

30Oct
Oct 30th 2020
16:00 to 17:00

Venue:

https://maths-cam-ac-uk.zoom.us/j/92821218455?pwd=aHFOZWw5bzVReUNYR2d5OWc1Tk15Zz09

Speaker:

Dominik Rothenhaeusler (Stanford University)

Series:

Statistics