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  1. 1. Valid causal inference in high-dimensional and complex settings

    Författare :Niloofar Moosavi; Xavier de Luna; Jenny Häggström; Edward Kennedy; Umeå universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Causal inference; high dimension; sensitivity analysis; variable selection; convolutional neural network; semiparametric efficiency bound; Statistics; statistik;

    Sammanfattning : The objective of this thesis is to consider some challenges that arise when conducting causal inference based on observational data. High dimensionality can occur when it is necessary to adjust for many covariates, and flexible models must be used to meet convergence assumptions. The latter may require the use of a novel machine learning estimator. LÄS MER