5 papers
Selective Inference in Graphical Models via Maximum Likelihood
Sofia Guglielmini, Gerda Claeskens, Snigdha Panigrahi
The graphical lasso is a widely used algorithm for fitting undirected Gaussian graphical models. However, for inference on functionals of edge values in the learned graph, standard…
Inference with Randomized Regression Trees
Soham Bakshi, Yiling Huang, Snigdha Panigrahi +1
Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical infe…
Selective Inference for Time-Varying Moderated Effects
Soham Bakshi, Walter Dempsey, Snigdha Panigrahi
Causal effect moderation investigates how the effect of interventions (or treatments) on outcome variables changes based on observed characteristics of individuals, known as potent…
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil Saeed, Snigdha Panigrahi, Caroline Uhler
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such…
Kinematic Formula for Heterogeneous Gaussian Related Fields
Snigdha Panigrahi, Jonathan Taylor, Sreekar Vadlamani
We provide a generalization of the Gaussian Kinematic Formula (GKF) in Taylor(2006) for multivariate, heterogeneous Gaussian-related fields. The fields under consideration are non-…