PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables

• URL: http://arxiv.org/abs/2606.02017v1
• Date: Mon, 01 Jun 2026 10:07:53 GMT
• Title: PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
• Abstract summary: We introduce PliableBVS, a variable selection approach that preserves the hierarchical structure of the pliable lasso.<n>The proposed model combines the continuous shrinkage effect of Bayesian lasso with a hierarchical spike-and-slab prior formulation.<n>In simulation studies the proposed method outperforms the original pliable lasso in identifying active main and interaction effects.
• Score: 1.0680378628718095
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: High-dimensional interaction models are useful for studying, for example, how a large set of variables of interest, such as gene expression or other omics features, interact with a smaller set of modifying variables, such as clinical covariates. In this context, the pliable lasso has recently been proposed as an efficient method for screening large numbers of potential interaction terms under an asymmetric weak hierarchical constraint. In this work, we extend this framework by introducing PliableBVS, a Bayesian variable selection approach that preserves the hierarchical structure of the pliable lasso while inducing sparsity through spike-and-slab priors. The proposed model combines the continuous shrinkage effect of Bayesian lasso with a hierarchical spike-and-slab prior formulation that has two layers of decision variables: one governing the inclusion of main effects and another controlling the inclusion of interaction effects which is conditional on the inclusion of the corresponding main effects. This structure enables simultaneous selection of high-dimensional main and interaction effects within a coherent probabilistic framework. In simulation studies the proposed method outperforms the original pliable lasso in identifying active main and interaction effects, reducing false discoveries, and improving prediction accuracy in most scenarios. Applications with data from a labor onset study and a preeclampsia study demonstrate that PliableBVS selects biologically meaningful features and interactions.

Related papers

• Score-Based Causal Discovery of Latent Variable Causal Models [44.676955616247334]
  We show that a properly formulated scoring function can achieve score equivalence and consistency for structure learning of latent variable causal models.<n>We further provide a characterization of the degrees of freedom for the marginal over the observed variables under multiple structural assumptions considered in the literature.
  arXiv  Detail & Related papers  (2026-05-19T18:44:43Z)
• Identification of Causal Direction under an Arbitrary Number of Latent Confounders [54.76982125821112]
  In real-world scenarios, observed variables may be affected by multiple latent variables simultaneously.<n>We make use of the joint higher-order cumulant matrix of the observed variables constructed in a specific way.<n>We show that, surprisingly, causal asymmetry between two observed variables can be directly seen from the rank deficiency properties of such higher-order cumulant matrices.
  arXiv  Detail & Related papers  (2025-10-26T15:10:00Z)
• Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability [16.072769293135305]
  We propose the Sparse Deep Model with Interactions (SDAMI), a framework that combines sparsity driven feature selection with deepworks for flexible function approximation.<n>Central to SDAMI is the concept of an Effect Footprint, which assumes that higher order interactions project marginally onto main effects.<n>For each selected main effect, SDAMI constructs a dedicated subnetwork, enabling nonlinear function approximation while preserving interpretability.
  arXiv  Detail & Related papers  (2025-09-27T02:44:57Z)
• Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses [0.0]
  We propose a pliable lasso that places sparsity-inducing priors, such as the horseshoe, on both main and interaction effects.<n>Our framework yields sparse, interpretable interaction structures, and principled measures of uncertainty.<n>Our method is implemented in the package texttthspliable available on Github.
  arXiv  Detail & Related papers  (2025-09-09T08:28:21Z)
• Bayesian Hybrid Machine Learning of Gallstone Risk [0.0]
  Gallstone disease is a complex, multifactorial condition with significant global health burdens.<n>We propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection.<n>This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.
  arXiv  Detail & Related papers  (2025-06-17T14:19:02Z)
• Active learning for affinity prediction of antibodies [45.58662352490961]
  For large molecules such as antibodies, identifying mutations that enhance antibody affinity is challenging. FERB methods can offer valuable insights into how different mutations will impact the potency and selectivity of a drug candidate. We present an active learning framework that iteratively proposes sequences for simulators to evaluate, thereby accelerating the search for improved binders.
  arXiv  Detail & Related papers  (2024-06-11T13:42:49Z)
• Score-based Causal Representation Learning with Interventions [54.735484409244386]
  This paper studies the causal representation learning problem when latent causal variables are observed indirectly. The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables.
  arXiv  Detail & Related papers  (2023-01-19T18:39:48Z)
• CausalDialogue: Modeling Utterance-level Causality in Conversations [83.03604651485327]
  We have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. We propose a causality-enhanced method called Exponential Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models.
  arXiv  Detail & Related papers  (2022-12-20T18:31:50Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? [1.1050303097572156]
  We show that both methods can be understood in terms of the same parameters and confounding assumptions under L2 loss. In the randomized setting, both approaches performed akin to the new blended versions in a benchmark study.
  arXiv  Detail & Related papers  (2022-06-21T12:45:07Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• Sparse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation [0.0]
  This paper develops a sparsity-inducing version of Bayesian Causal Forests. It is designed to estimate heterogeneous treatment effects using observational data.
  arXiv  Detail & Related papers  (2021-02-12T15:24:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.