6 citations · 9 across the 5 of their papers we have counts for
6 papers
Doubly robust estimation with functional outcomes missing at random
Xijia Liu, Kreske Felix Ecker, Lina Schelin +1
We present and study semi-parametric estimators for the mean of functional outcomes in situations where some of these outcomes are missing and covariate information is available on…
Causal inference targeting a concentration index for studies of health inequalities
Mohammad Ghasempour, Xavier de Luna, Per E. Gustafsson
A concentration index, a standardized covariance between a health variable and relative income ranks, is often used to quantify income-related health inequalities. There is a lack…
Convolutional neural networks for valid and efficient causal inference
Mohammad Ghasempour, Niloofar Moosavi, Xavier de Luna
Convolutional neural networks (CNN) have been successful in machine learning applications. Their success relies on their ability to consider space invariant local features. We cons…
SDRcausal: an R package for causal inference based on sufficient dimension reduction
Mohammad Ghasempour, Xavier de Luna
SDRcausal is a package that implements sufficient dimension reduction methods for causal inference as proposed in Ghosh, Ma, and de Luna (2021). The package implements (augmented)…
Robust semiparametric inference with missing data
Eva Cantoni, Xavier de Luna
Classical semiparametric inference with missing outcome data is not robust to contamination of the observed data and a single observation can have arbitrarily large influence on es…
A Consistency Result for Bayes Classifiers with Censored Response Data
Priyantha Wijayatunga, Xavier de Luna
Naive Bayes classifiers have proven to be useful in many prediction problems with complete training data. Here we consider the situation where a naive Bayes classifier is trained w…