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Found 15 papers, 9 papers with code
Model free variable importance for high dimensional data
Here we present some model-free methods that do not require access to the prediction function.
Variable importance without impossible data
The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple subjects.
Deletion and Insertion Tests in Regression Models
We find an expression for the expected value of the AUC under a random ordering of inputs to $f$ and propose an alternative area above a straight line for the regression setting.
Additive models
Explainable Artificial Intelligence (XAI)
+1
Probing neural networks with t-SNE, class-specific projections and a guided tour
We use graphical methods to probe neural nets that classify images.
What makes you unique?
We use Shapley value to combine all of the reductions in log cardinality due to revealing a variable after some subset of the other variables has been revealed.
Cohort Shapley value for algorithmic fairness
Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations.
Quasi-Newton Quasi-Monte Carlo for variational Bayes
Many machine learning problems optimize an objective that must be measured with noise.
Efficient estimation of the ANOVA mean dimension, with an application to neural net classification
For an additive function the radial and winding stairs are most efficient.
Explaining black box decisions by Shapley cohort refinement
Instead of changing the value of a predictor we include or exclude subjects similar to the target subject on that predictor to form a similarity cohort.
Deterministic parallel analysis
This paper presents a deterministic version of PA (DPA), which is faster and more reproducible than PA. We show that DPA selects large factors and does not select small factors just like [Dobriban, 2017] shows for PA.
Methodology
Detecting Multiple Replicating Signals using Adaptive Filtering Procedures
Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, study populations, across time etc.
Methodology
Efficient moment calculations for variance components in large unbalanced crossed random effects models
Large crossed data sets, described by generalized linear mixed models, have become increasingly common and provide challenges for statistical analysis.
Methodology Computation
Statistically efficient thinning of a Markov chain sampler
That observation is often taken to mean that thinning MCMC output cannot improve statistical efficiency.
Confounder Adjustment in Multiple Hypothesis Testing
In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e. g. treatment variable, phenotype) and the outcome.
Methodology Statistics Theory Statistics Theory 62H25, 62J15
Optimal Multiple Testing Under a Gaussian Prior on the Effect Sizes
For a Gaussian prior on effect sizes, we show that finding the optimal weights is a non-convex problem.
Methodology
